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Background: Ebola virus disease (hereafter EVD or Ebola) has a high fatality rate. The devastating effects of the current epidemic of Ebola in West Africa have put the global health response in acute focus. In response, the World Health Organization (WHO) has declared the Ebola outbreak in West Africa as a "Public Health Emergency of International Concern". A small proportion of scientific literature is dedicated to Ebola research. Methods: To identify global research trends in Ebola research, the Institute for Scientific Information (ISI) Web of ScienceTM database was used to search for data, which encompassed original articles published from 1900 to 2013. The keyword "Ebola" was used to identify articles for the purposes of this review. In order to include all published items, the database was searched using the Basic Search method. Results: The earliest record of literature about Ebola indexed in the Web of Science is from 1977. A total of 2477 publications on Ebola, published between 1977 and 2014 (with the number of publications increasing annually), were retrieved from the database. Original research articles (n = 1623, 65.5 %) were the most common type of publication. Almost all (96.5 %) of the literature in this field was in English. The USA had the highest scientific output and greatest number of funding agencies. Journal of Virology published 239 papers on Ebola, followed by Journal of Infectious Diseases and Virology, which published 113 and 99 papers, respectively. A total of 1911 papers on Ebola were cited 61,477 times. Conclusion: This analysis identified the current state of research and trends in studies about Ebola between 1977 and 2014. Our bibliometric analysis provides a historical perspective on the progress in Ebola research. [ABSTRACT FROM AUTHOR]

Background: In 1982, the Annals of Virology published a paper showing how Liberia has a highly endemic potential of Ebola warning health authorities of the risk for potential outbreaks; this journal is only available by subscription. Limiting the accessibility of such knowledge may have reduced information propagation toward public health actors who were indeed surprised by and unprepared for the 2014 epidemic. Open access (OA) publication can allow for increased access to global health research (GHR). Our study aims to assess the use, cost and impact of OA diffusion in the context of GHR.Method: A total of 3366 research articles indexed under the Medical Heading Subject Heading "Global Health" published between 2010 and 2014 were retrieved using PubMed to (1) quantify the uptake of various types of OA, (2) estimate the article processing charges (APCs) of OA, and (3) analyse the relationship between different types of OA, their scholarly impact and gross national income per capita of citing countries.Results: Most GHR publications are not available directly on the journal's website (69%). Further, 60.8% of researchers do not self-archive their work even when it is free and in keeping with journal policy. The total amount paid for APCs was estimated at US$1.7 million for 627 papers, with authors paying on average US$2732 per publication; 94% of APCs were paid to journals owned by the ten most prominent publication houses from high-income countries. Researchers from low- and middle-income countries are generally citing less expensive types of OA, while researchers in high-income countries are citing the most expensive OA.Conclusions: Although OA may help in building global research capacity in GHR, the majority of publications remain subscription only. It is logical and cost-efficient for institutions and researchers to promote OA by self-archiving publications of restricted access, as it not only allows research to be cited by a broader audience, it also augments citation rates. Although OA does not ensure full knowledge transfer from research to practice, limiting public access can negatively impact implementation and outcomes of health policy and reduce public understanding of health issues. [ABSTRACT FROM AUTHOR]

In this paper, we propose a numerical algorithm to obtain the optimal epidemic parameters for a time-dependent Susceptible-Unidentified infected-Confirmed (tSUC) model. The tSUC model was developed to investigate the epidemiology of unconfirmed infection cases over an extended period. Among the epidemic parameters, the transmission rate can fluctuate significantly or remain stable due to various factors. For instance, if early intervention in an epidemic fails, the transmission rate may increase, whereas appropriate policies, including strict public health measures, can reduce the transmission rate. Therefore, we adaptively estimate the transmission rate to the given data using the linear change points of the number of new confirmed cases by the given cumulative confirmed data set, and the time-dependent transmission rate is interpolated based on the estimated transmission rates at linear change points. The proposed numerical algorithm preprocesses actual cumulative confirmed cases in India to smooth it and uses the preprocessed data to identify linear change points. Using these linear change points and the tSUC model, it finds the optimal time-dependent parameters that minimize the difference between the actual cumulative confirmed cases and the computed numerical solution in the least-squares sense. Numerical experiments demonstrate the numerical solution of the tSUC model using the optimal time-dependent parameters found by the proposed algorithm, validating the performance of the algorithm. Consequently, the proposed numerical algorithm calculates the time-dependent transmission rate for the actual cumulative confirmed cases in India, which can serve as a basis for analyzing the COVID-19 pandemic in India. [ABSTRACT FROM AUTHOR]

Background: Since the outbreak of the COVID-19 pandemic, the excess mortality P-score has gained prominence as a measure of pandemic burden. The P-score indicates the percentage by which observed deaths deviate from expected deaths. As the P-score is regularly used to compare excess mortality between countries, questions arise regarding the age dependency of the measure. In this paper we present formal and empirical results on the population structure bias of the P-score with a special focus on cross-country comparisons during the COVID-19 pandemic in Europe. Methods: P-scores were calculated for European countries for 2021, 2022, and 2023 using data from the 2024 revision of the United Nations' World Population Prospects and the HMDs Short Term Mortality Fluctuations data series. The expected deaths for 2021, 2022, and 2023 were estimated using a Lee–Carter forecast model assuming pre-pandemic conditions. P-score differences between countries were decomposed using a Kitagawa-type decomposition into excess-mortality and structural components. To investigate the sensitivity of P-score cross-country rankings to differences in population structure we calculated the rank-correlation between age-standardized and classical P-scores. Results: The P-score is an average of age-specific percent excess deaths weighted by the age-distribution of expected deaths. It can be shown that the effect of differences in the distribution of deaths only plays a marginal role in a European comparison. In most cases, the excess mortality effect is the dominant effect. P-score rankings among European countries during the COVID-19 pandemic are similar under both age-standardized and classical P-scores. Conclusions: Although the P-score formally depends on the age-distribution of expected deaths, this structural component only plays a minor role in a European comparison, as the distribution of deaths across the continent is similar. Thus, the P-score is suitable as a measure of excess mortality in a European comparison, as it mainly reflects the differences in excess mortality. However, this finding should not be extrapolated to global comparisons, where countries could have very different death distributions. In situations were P-score comparisons are biased age-standardization can be applied as a solution. [ABSTRACT FROM AUTHOR]

Background: The new European Medicines Agency (EMA) Clinical Trials Information System (CTIS), based on the Clinical Trials Regulation (CTR EU 536/2014), came into full effect on 31 January 2022 and was intended to provide an easier, more streamlined approach to the registration of clinical trials taking place in Europe. Using the experience gained on the new regulatory framework from three multi-national European clinical research studies of outbreak-prone infectious diseases, this article describes the advantages and shortcomings of the new clinical trial submission procedure. Methods: We report the time to approval, size of the application dossier, and number of requests for information (RFIs) for each study. We also explore the experience of each study within the regulatory framework and its use of CTIS to document the real-world, practical consequences of the system on individual studies. The study assesses the experience of three multi-country studies conducted in Europe working within the EU and non-EU regulatory environments. Results: While the time to regulatory and ethical approval has improved since the implementation of the new regulation, the timelines for approvals are still unacceptably slow, particularly for studies being conducted in the context of an evolving outbreak. Within the new regulatory approval procedure, there is evidence of conflicting application requirements, increased document burden, barriers to submitting important modifications, and debilitating technical hurdles. Conclusions: CTIS promised to lower the administrative bar, but unfortunately this has not been achieved. There are challenges that need to be urgently confronted and addressed for international research collaborators to effectively manage health crises in the future. While the value of multi-national outbreak research is clear, the limitations and delays imposed by the system, which raise challenging ethical questions about the regulation, are prejudicial to all clinical research, especially publicly funded academic studies. This report is relevant to both regulators and clinical researchers. It is hoped that these findings can help improve pan-European clinical trials, especially for the purpose of epidemic preparedness and response. Trial registration: This paper references experiences gained during management of three pan-European trials: EU-SolidAct's Bari-SolidAct (CT No. 2022-500385-99-00 - 15 March 2022) and AXL-SolidAct (CT No. 2022-500363-12-00 - 19 April 2022), and MOSAIC (CT No. 2022-501132-42-00 - 22 June 2022). [ABSTRACT FROM AUTHOR]

Background: With regard to the coronavirus disease (COVID-19) pandemic in Madagascar, little is known about the knowledge, the perceptions and the impacts of this disease on women of childbearing age. People's knowledge of COVID-19 can have an impact on their attitudes towards seeking care. The aim of the current study is to determine the knowledge of COVID-19 and associated determinants among women of childbearing age in Moramanga. Methods: A cross-sectional study based on questionnaire administration was used among women of childbearing age. Data collection was conducted from August to October 2021. A scoring method was applied to evaluate their knowledge level and perceptions about COVID-19 and its impacts on their lives. A binary stepwise logistic regression was performed to determine the sociodemographic determinants of their knowledge level about COVID-19. Results: A total of 885 women of childbearing age from urban and rural Moramanga areas were interviewed. Approximately 49.8% (441/885) lived in urban areas, and 50.2% (444/885) lived in rural areas. Approximately 35.3% (322/885) of the participants had a good level of knowledge of COVID-19. Multivariate analysis showed that the probability of having a good level of knowledge of COVID-19 had a significant statistical association (p value < 0.05) with living in an urban area [AOR: 2.89; 95% CI (1.89–4.42)], telephone ownership [AOR: 1.71; 95% CI (1.16–2.53)], radio ownership [AOR 2.2; 95% CI (1.43–3.38)], watching TV [AOR = 1.95; 95% CI (1.34–2.83)] and reading journal papers [AOR = 3.74 95% CI (1.69–8.27)]. Conclusions: Almost a third of the sampled women of childbearing age had a good level of knowledge of COVID-19. Access to information through telecommunications technologies increases the chances of being better informed about the disease. To avoid the negative repercussions of infectious disease epidemics, it is necessary to improve the awareness of childbearing women about these diseases by taking demographic features of the population into account. [ABSTRACT FROM AUTHOR]

Background: Epidemics or pandemics, such as the current Coronavirus Disease 2019 (COVID-19) crisis, pose unique challenges to healthcare professionals (HCPs). Caring for patients during an epidemic/pandemic may impact negatively on the mental health of HCPs. There is a lack of evidence-based advice on what would be effective in mitigating this impact.Objectives: This rapid review synthesizes the evidence on the psychological impact of pandemics/epidemics on the mental health of HCPs, what factors predict this impact, and the evidence of prevention/intervention strategies to reduce this impact.Method: According to rapid review guidelines, systematic searches were carried out in Embase.com , PubMed, APA PsycINFO-Ovid SP, and Web of Science (core collection). Searches were restricted to the years 2003 or later to ensure inclusion of the most recent epidemic/pandemics, such as Severe Acute Respiratory Syndrome (SARS). Papers written in French or English, published in peer-reviewed journals, and of quantitative design using validated measures of mental health outcomes were included. Of 1308 papers found, 50 were included. The full protocol for this rapid review was registered with Prospero (reg.no. CRD42020175985).Results: Results show that exposed HCPs working with patients during an epidemic/pandemic are at heightened risk of mental health problems in the short and longer term, particularly: psychological distress, insomnia, alcohol/drug misuse, and symptoms of posttraumatic stress disorder (PTSD), depression, anxiety, burnout, anger, and higher perceived stress. These mental health problems are predicted by organizational, social, personal, and psychological factors and may interfere with the quality of patient care. Few evidence-based early interventions exist so far.Discussion: HCPs need to be provided with psychosocial support to protect their mental wellbeing if they are to continue to provide high quality patient care. Several recommendations relevant during and after an epidemic/pandemic, such as COVID-19, and in preparation for a future outbreak, are proposed. [ABSTRACT FROM AUTHOR]

In this paper, we propose a parameter identification methodology of the SIRD model, an extension of the classical SIR model, that considers the deceased as a separate category. In addition, our model includes one parameter which is the ratio between the real total number of infected and the number of infected that were documented in the official statistics. Due to many factors, like governmental decisions, several variants circulating, opening and closing of schools, the typical assumption that the parameters of the model stay constant for long periods of time is not realistic. Thus our objective is to create a method which works for short periods of time. In this scope, we approach the estimation relying on the previous 7 days of data and then use the identified parameters to make predictions. To perform the estimation of the parameters we propose the average of an ensemble of neural networks. Each neural network is constructed based on a database built by solving the SIRD for 7 days, with random parameters. In this way, the networks learn the parameters from the solution of the SIRD model. Lastly we use the ensemble to get estimates of the parameters from the real data of Covid19 in Romania and then we illustrate the predictions for different periods of time, from 10 up to 45 days, for the number of deaths. The main goal was to apply this approach on the analysis of COVID-19 evolution in Romania, but this was also exemplified on other countries like Hungary, Czech Republic and Poland with similar results. The results are backed by a theorem which guarantees that we can recover the parameters of the model from the reported data. We believe this methodology can be used as a general tool for dealing with short term predictions of infectious diseases or in other compartmental models. [ABSTRACT FROM AUTHOR]

Introduction: Africa was threatened by the coronavirus disease 2019 (COVID-19) due to the limited health care infrastructure. Rwanda has consistently used non-pharmaceutical strategies, such as lockdown, curfew, and enforcement of prevention measures to control the spread of COVID-19. Despite the mitigation measures taken, the country has faced a series of outbreaks in 2020 and 2021. In this paper, we investigate the nature of epidemic phenomena in Rwanda and the impact of imported cases on the spread of COVID-19 using endemic-epidemic spatio-temporal models. Our study provides a framework for understanding the dynamics of the epidemic in Rwanda and monitoring its phenomena to inform public health decision-makers for timely and targeted interventions. Results: The findings provide insights into the effects of lockdown and imported infections in Rwanda's COVID-19 outbreaks. The findings showed that imported infections are dominated by locally transmitted cases. The high incidence was predominant in urban areas and at the borders of Rwanda with its neighboring countries. The inter-district spread of COVID-19 was very limited due to mitigation measures taken in Rwanda. Conclusion: The study recommends using evidence-based decisions in the management of epidemics and integrating statistical models in the analytics component of the health information system. [ABSTRACT FROM AUTHOR]

Background: Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.Methods: We systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.Results: We identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.Conclusions: IBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent "branding". Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come. [ABSTRACT FROM AUTHOR]

In January 2020, SARS-CoV-2 virus was identified as a cause of an outbreak in China. The disease quickly spread worldwide, and the World Health Organization (WHO) declared the pandemic in March 2020. From the first notifications of spread of the disease, the WHO's Emergency Programme implemented a global COVID-19 surveillance system in coordination with all WHO regional offices. The system aimed to monitor the spread of the epidemic over countries and across population groups, severity of the disease and risk factors, and the impact of control measures. COVID-19 surveillance data reported to WHO is a combination of case-based data and weekly aggregated data, focusing on a minimum global dataset for cases and deaths including disaggregation by age, sex, occupation as a Health Care Worker, as well as number of cases tested, and number of cases newly admitted for hospitalization. These disaggregations aim to monitor inequities in COVID-19 distribution and risk factors among population groups. SARS-CoV-2 epidemic waves continue to sweep the world; as of March 2022, over 445 million cases and 6 million deaths have been reported worldwide. Of these, over 327 million cases (74%) have been reported in the WHO surveillance database, of which 255 million cases (57%) are disaggregated by age and sex. A public dashboard has been made available to visualize trends, age distributions, sex ratios, along with testing and hospitalization rates. It includes a feature to download the underlying dataset. This paper will describe the data flows, database, and frontend public dashboard, as well as the challenges experienced in data acquisition, curation and compilation and the lessons learnt in overcoming these. Two years after the pandemic was declared, COVID-19 continues to spread and is still considered a Public Health Emergency of International Concern (PHEIC). While WHO regional and country offices have demonstrated tremendous adaptability and commitment to process COVID-19 surveillance data, lessons learnt from this major event will serve to enhance capacity and preparedness at every level, as well as institutional empowerment that may lead to greater sharing of public health evidence during a PHEIC, with a focus on equity. [ABSTRACT FROM AUTHOR]

Background: Community engagement (CE) is a well-established practical and scholarly field, recognised as core to the science and ethics of health research, for which researchers and practitioners have increasingly asked questions about desired standards and evaluation. In infectious disease outbreak contexts, questions may be more complex. However, it is unclear what body of knowledge has been developed for CE specifically as it applies to emerging infectious diseases. This scoping review seeks to describe (1) How CE has been conceptualised and understood; and (2) What conclusions have research teams reached on the effectiveness of CE in these settings, including challenges and facilitators.Methods: We used a scoping review framework by Arksey and O'Malley (Int J Soc Res Methodol 8:19-32, 2005) to structure our review. We conducted a brainstorming session and initial trial search to inform the protocol, search terms, and strategy. Three researchers discussed, developed and applied agreed screening tools and selection criteria to the final search results. Five researchers used the screening tools to screen abstracts and full text for inclusion by consensus. Additional publications were sought from references of retrieved publications and an expert call for literature. We analysed and reported emerging themes qualitatively.Results: We included 59 papers from a total of 722 articles derived from our trial and final literature searches, as well as a process of "citation chasing" and an expert call for grey literature. The core material related exclusively to health research trials during the 2014-2016 West Africa Ebola outbreak. We synthesized reports on components of effectiveness of CE to identify and propose three themes as essential elements of effective CE.Conclusions: While there is a large volume of literature documenting CE activities in infectious disease research settings generally, there are few accounts of effectiveness dimensions of CE. Our review proposes three themes to facilitate the effectiveness of CE initiatives as essential elements of CE activities in infectious diseases studies: (1) Communication towards building collaborative relationships; (2) Producing contextual knowledge; and (3) Learning lessons over time. As there were relatively few in-depth accounts of CE from our literature review, documentation and accounts of CE used in health research should be prioritised. [ABSTRACT FROM AUTHOR]

Background: Effective responses to the worsening drug overdose epidemic require accurate and timely drug overdose surveillance data. The objectives of this paper are to describe the development, functionality, and accuracy of the Suspected Potential Overdose Tracker (SPOT) for predicting accidental drug overdose as the cause and manner of death in near real-time, and public health implications of adopting the tool.Methods: SPOT was developed to rapidly identify overdose deaths through a simple and duplicable process using data collected by death investigators. The tool assigns each death a ranking of 1 through 3 based on the likelihood of it being an unintentional drug overdose, with 1 representing the highest likelihood that the death will be confirmed as an unintentional drug overdose and 3 representing the lowest. We measured the accuracy of the tool for predicting overdose deaths by comparing potential overdose deaths in New York City from 2018-2020 that were identified using SPOT to finalized death certificates. We also calculated the proportion of death certificate-confirmed overdoses that were missed by the SPOT tool and the proportion of type 1 errors.Results: SPOT captured up to 77% of unintentional drug overdose deaths using data collected within 72 h of fatality. The tool predicted unintentional drug overdose from 2018 to 2020 with 93-97% accuracy for cases assigned a ranking of 1, 87-91% accuracy for cases assigned a ranking of 2, and 62-73% accuracy for cases assigned a ranking of 3. Among all unintentional overdose deaths in 2018, 2019, and 2020, 21%, 28%, and 33% were missed by the SPOT tool, respectively. During this timeframe, the proportion of type 1 errors ranged from 15%-23%.Conclusions: SPOT may be used by health departments, epidemiologists, public health programs, and others to monitor overdose fatalities before death certificate data becomes available. Improved monitoring of overdose fatalities allows for rapid data-driven decision making, identification of gaps in public health and public safety overdose response, and evaluation and response to overdose prevention interventions, programs, and policies. [ABSTRACT FROM AUTHOR]

Background: In the context of the massive scale up of malaria interventions, there is increasing recognition that the current capacity of routine malaria surveillance conducted in most African countries through integrated health management information systems is inadequate. The timeliness of reporting to higher levels of the health system through health management information systems is often too slow for rapid action on focal infectious diseases such as malaria. The purpose of this paper is to: 1) describe the implementation of a malaria sentinel surveillance system in Ethiopia to help fill this gap; 2) describe data use for epidemic detection and response as well as programmatic decision making; and 3) discuss lessons learned in the context of creating and running this system. Case description: As part of a comprehensive strategy to monitor malaria trends in Oromia Regional State, Ethiopia, a system of ten malaria sentinel sites was established to collect data on key malaria morbidity and mortality indicators. To ensure the sentinel surveillance system provides timely, actionable data, the sentinel facilities send aggregate data weekly through short message service (SMS) to a central database server. Bland-Altman plots and Poisson regression models were used to investigate concordance of malaria indicator reports and malaria trends over time, respectively. Discussion: This paper describes three implementation challenges that impacted system performance in terms of: 1) ensuring a timely and accurate data reporting process; 2) capturing complete and accurate patient-level data; and 3) expanding the usefulness and generalizability of the system's data to monitor progress towards the national malaria control goals of reducing malaria deaths and eventual elimination of transmission. Conclusions: The use of SMS for reporting surveillance data was identified as a promising practice for accurately tracking malaria trends in Oromia. The rapid spread of this technology across Africa offers promising opportunities to collect and disseminate surveillance data in a timely way. High quality malaria surveillance in Ethiopia remains a resource intensive activity and extending the generalizability of sentinel surveillance findings to other contexts remains a major limitation of these strategies. [ABSTRACT FROM AUTHOR]

Background: The 2020-2021 coronavirus (Covid-19) pandemic is just the latest epidemic event that requires us to rethink and change our understanding of health. Health should no longer be conceived only in relation to human beings, but in unitary terms, as a dimension that connects humans, animals, plants, and the environment (holistic view, One Health). In general, alterations occurring in this articulated chain of life trigger a domino effect.Methodology: In this paper, we review the One Health paradigm in the light of the Covid-19 pandemic and distinguish two approaches within it that might be dubbed the Prudent one and the Radical one. Each approach is structured in three levels - epistemological, medical, and ethical.Results: In this way, we show how we humans can better address the pandemic today and how, in the future, we can treat the whole living system better, by renouncing our anthropocentric perspective on health.Conclusion: We hold that the Prudent approach can be very helpful, and we discuss the medical and ethical issues related to it. We also consider the Radical view and the epistemological turn it requires compared to the Prudent one. [ABSTRACT FROM AUTHOR]

Background: COVID-19 has developed into a worldwide pandemic which was accompanied by an «infodemic» consisting of much false and misleading information. To cope with these new challenges, health literacy plays an essential role. The aim of this paper is to present the findings of a trend study in Switzerland on corona-specific health literacy, the use of and trust in information sources during the COVID-19 pandemic, and their relationships.Methods: Three online surveys each with approximately 1'020 individuals living in the German-speaking part of Switzerland (age ≥ 18 years) were conducted at different timepoints during the COVID-19 pandemic, namely spring, fall and winter 2020. For the assessment of corona-specific health literacy, a specifically developed instrument (HLS-COVID-Q22) was used. Descriptive, bivariate, and multivariate data analyses have been conducted.Results: In general, a majority of the Swiss-German population reported sufficient corona-specific health literacy levels which increased during the pandemic: 54.6% participants in spring, 62.4% in fall and 63.3% in winter 2020 had sufficient corona-specific health literacy. Greatest difficulties concerned the appraisal of health information on the coronavirus. The most used information sources were television (used by 73.3% in spring, 70% in fall and 72.3% in winter) and the internet (used by 64.1, 64.8 and 66.5%). Although health professionals, health authorities and the info-hotline were rarely mentioned as sources for information on the coronavirus, respondents had greatest trust in them. On the other hand, social media were considered as the least trustworthy information sources. Respondents generally reporting more trust in the various information sources, tended to have higher corona-specific health literacy levels.Conclusions: Sufficient health literacy is an essential prerequisite for finding, understanding, appraising, and applying health recommendations, particularly in a situation where there is a rapid spread of a huge amount of information. The population should be supported in their capability in appraising the received information and in assessing the trustworthiness of different information sources. [ABSTRACT FROM AUTHOR]

Background: There has been little research on the impact of the 2014-2015 West African Ebola crisis on people with disabilities. This paper outlines the way in which the Ebola Virus Disease (EVD) outbreak in Liberia in 2015 highlighted existing inequalities and exclusion of people with disabilities and their households. Methods: The results presented here are part of a larger ESRC/DFID-funded mixed methods research project in Liberia (2014-2017) which included a quantitative household survey undertaken in five counties, complemented by qualitative focus group discussions and interviews with people with disabilities and other key stakeholders. Uniquely, this research gathered information about people with disabilities' experience of the EVD outbreak, as well as additional socioeconomic and inclusion data, that compared their experience with non-disabled community members. Results: Reflections by people with disabilities themselves show knowledge, preparation, and responses to the EVD epidemic was often markedly different among people with disabilities due to limited resources, lack of inclusion by many mainstream public health and medical interventions and pre-existing discrimination, marginalisation and exclusion. Interviews with other key stakeholder revealed a lack of awareness of disability issues or sufficient training to include this population systematically in both Ebola response activities and general health services. Key findings include the need to understand and mitigate direct and indirect health consequences of unequal responses to the epidemic, as well as the limited capacity of healthcare and social services to respond to people with disabilities. Conclusion: There are lessons to be learned from Ebola outbreak around inclusion of people with disabilities, relevant to the current COVID-19 pandemic. Now is the time to undertake measures to ensure that people with disabilities do not continue to be marginalised and excluded during global public health emergencies. [ABSTRACT FROM AUTHOR]

Background: The importance of integrating the social sciences in epidemic preparedness and response has become a common feature of infectious disease policy and practice debates. However to date, this integration remains inadequate, fragmented and under-funded, with limited reach and small initial investments. Based on data collected prior to the COVID-19 pandemic, in this paper we analysed the variety of knowledge, infrastructure and funding gaps that hinder the full integration of the social sciences in epidemics and present a strategic framework for addressing them.Methods: Senior social scientists with expertise in public health emergencies facilitated expert deliberations, and conducted 75 key informant interviews, a consultation with 20 expert social scientists from Africa, Asia and Europe, 2 focus groups and a literature review of 128 identified high-priority peer reviewed articles. We also analysed 56 interviews from the Ebola 100 project, collected just after the West African Ebola epidemic. Analysis was conducted on gaps and recommendations. These were inductively classified according to various themes during two group prioritization exercises. The project was conducted between February and May 2019. Findings from the report were used to inform strategic prioritization of global investments in social science capacities for health emergencies.Findings: Our analysis consolidated 12 knowledge and infrastructure gaps and 38 recommendations from an initial list of 600 gaps and 220 recommendations. In developing our framework, we clustered these into three areas: 1) Recommendations to improve core social science response capacities, including investments in: human resources within response agencies; the creation of social science data analysis capacities at field and global level; mechanisms for operationalizing knowledge; and a set of rapid deployment infrastructures; 2) Recommendations to strengthen applied and basic social sciences, including the need to: better define the social science agenda and core competencies; support innovative interdisciplinary science; make concerted investments in developing field ready tools and building the evidence-base; and develop codes of conduct; and 3) Recommendations for a supportive social science ecosystem, including: the essential foundational investments in institutional development; training and capacity building; awareness-raising activities with allied disciplines; and lastly, support for a community of practice.Interpretation: Comprehensively integrating social science into the epidemic preparedness and response architecture demands multifaceted investments on par with allied disciplines, such as epidemiology and virology. Building core capacities and competencies should occur at multiple levels, grounded in country-led capacity building. Social science should not be a parallel system, nor should it be "siloed" into risk communication and community engagement. Rather, it should be integrated across existing systems and networks, and deploy interdisciplinary knowledge "transversally" across all preparedness and response sectors and pillars. Future work should update this framework to account for the impact of the COVID-19 pandemic on the institutional landscape. [ABSTRACT FROM AUTHOR]

Background: Syndromic surveillance provides public health intelligence to aid in early warning and monitoring of public health impacts (e.g. seasonal influenza), or reassurance when an impact has not occurred. Using information collected during routine patient care, syndromic surveillance can be based on signs/symptoms/preliminary diagnoses. This approach makes syndromic surveillance much timelier than surveillance requiring laboratory confirmed diagnoses. The provision of healthcare services and patient access to them varies globally. However, emergency departments (EDs) exist worldwide, providing unscheduled urgent care to people in acute need. This provision of care makes ED syndromic surveillance (EDSyS) a potentially valuable tool for public health surveillance internationally. The objective of this study was to identify and describe the key characteristics of EDSyS systems that have been established and used globally.Methods: We systematically reviewed studies published in peer review journals and presented at International Society of Infectious Disease Surveillance conferences (up to and including 2017) to identify EDSyS systems which have been created and used for public health purposes. Search criteria developed to identify "emergency department" and "syndromic surveillance" were applied to NICE healthcare, Global Health and Scopus databases.Results: In total, 559 studies were identified as eligible for inclusion in the review, comprising 136 journal articles and 423 conference abstracts/papers. From these studies we identified 115 EDSyS systems in 15 different countries/territories across North America, Europe, Asia and Australasia. Systems ranged from local surveillance based on a single ED, to comprehensive national systems. National EDSyS systems were identified in 8 countries/territories: 2 reported inclusion of ≥85% of ED visits nationally (France and Taiwan).Conclusions: EDSyS provides a valuable tool for the identification and monitoring of trends in severe illness. Technological advances, particularly in the emergency care patient record, have enabled the evolution of EDSyS over time. EDSyS reporting has become closer to 'real-time', with automated, secure electronic extraction and analysis possible on a daily, or more frequent basis. The dissemination of methods employed and evidence of successful application to public health practice should be encouraged to support learning from best practice, enabling future improvement, harmonisation and collaboration between systems in future.Prospero Number: CRD42017069150 . [ABSTRACT FROM AUTHOR]

Background: Over the past few decades, numerous forecasting methods have been proposed in the field of epidemic forecasting. Such methods can be classified into different categories such as deterministic vs. probabilistic, comparative methods vs. generative methods, and so on. In some of the more popular comparative methods, researchers compare observed epidemiological data from the early stages of an outbreak with the output of proposed models to forecast the future trend and prevalence of the pandemic. A significant problem in this area is the lack of standard well-defined evaluation measures to select the best algorithm among different ones, as well as for selecting the best possible configuration for a particular algorithm.Results: In this paper we present an evaluation framework which allows for combining different features, error measures, and ranking schema to evaluate forecasts. We describe the various epidemic features (Epi-features) included to characterize the output of forecasting methods and provide suitable error measures that could be used to evaluate the accuracy of the methods with respect to these Epi-features. We focus on long-term predictions rather than short-term forecasting and demonstrate the utility of the framework by evaluating six forecasting methods for predicting influenza in the United States. Our results demonstrate that different error measures lead to different rankings even for a single Epi-feature. Further, our experimental analyses show that no single method dominates the rest in predicting all Epi-features when evaluated across error measures. As an alternative, we provide various Consensus Ranking schema that summarize individual rankings, thus accounting for different error measures. Since each Epi-feature presents a different aspect of the epidemic, multiple methods need to be combined to provide a comprehensive forecast. Thus we call for a more nuanced approach while evaluating epidemic forecasts and we believe that a comprehensive evaluation framework, as presented in this paper, will add value to the computational epidemiology community. [ABSTRACT FROM AUTHOR]

Background: The vulnerability approach suggests that disasters such as epidemics have different effects according not only to physical vulnerability but also to economic class (status). This paper examines the effect of the Middle East Respiratory Syndrome epidemic on the labor market to investigate whether vulnerable groups become more vulnerable due to an interaction between the socio-economic structure and physical risk. Methods: This paper examines the effect of the Middle East Respiratory Syndrome epidemic on the labor market by considering unemployment status, job status, working hours, reason for unemployment and underemployment status. In particular, the study investigates whether the U-shaped curve becomes a J-shaped curve due to the interaction between medical vulnerability and labor market vulnerability after an outbreak, assuming that the relative vulnerability in the labor market by age shows a U curve with peaks for the young group and middle aged and old aged groups using the Economically Active Population Survey. We use the difference in difference approach and also conduct a falsification check and robustness check. Results: The results suggest that older workers faced a higher possibility of unemployment after the Middle East Respiratory Syndrome outbreak. In particular, they experienced higher involuntary unemployment and underemployment status as well as decreased working hours. It was confirmed that the relative vulnerability of the labor market for older workers was higher than for the other age groups after the epidemic outbreak due to the double whammy of vulnerability in the medical and labor market. The vulnerability in the young group partially increased compared to the 30s and 40s age groups due to their relative vulnerability in the labor market despite being healthy. We find that assuming the relative vulnerability in the existing labor market shows a U shape with age increase, the U-shaped curve became J-shaped after the outbreak. Conclusions: Disasters like epidemics can occur unexpectedly and affect certain groups more than other. Therefore, medical protection should be enhanced for groups vulnerable to disease and economic measures are also required for the protection of their livelihoods in the labor market to prevent unemployment stemming from inequality. [ABSTRACT FROM AUTHOR]

Background: Swaziland is experiencing the world's worst HIV and AIDS epidemic. Prevalence rose from four percent of antenatal clinic attendees in 1992 to 42.6 percent in 2004. The Report 'Reviewing 'Emergencies' for Swaziland: Shifting the Paradigm in a New Era' published in 2007 bought together social and economic indicators. It built a picture of the epidemic as a humanitarian emergency, requiring urgent action from international organisations, donors, and governments. Following a targeted communications effort, the report was believed to have raised the profile of the issue and Swaziland - a success story for HIV and AIDS research.Methods: Keen to understand how, where and why the report had an impact, Health Economics and HIV/AIDS Research Division commissioned an assessment to track and evaluate the influence of the research. This tapped into literature on the significance of understanding the research-to-policy interface. This paper outlines the report and its impact. It explores key findings from the assessment and suggests lessons for future research projects.Results: The paper demonstrates that, although complex, and not without methodological issues, impact assessment of research can be of real value to researchers in understanding the research-to-policy interface.Conclusion: Only by gaining insight into this process can researchers move forward in delivering effective research. [ABSTRACT FROM AUTHOR]

The article discusses a study which presents the findings of an atypical systematic review of 60 years of literature to arrive at a more comprehensive awareness of the constructs of participation for communicable disease control and elimination and provide guidance for the current malaria elimination campaign. The papers included in the study were systematically coded using a pre-determined qualitative coding matrix that identified influences on community participation

Background: Iron metabolism and immune response to SARS-CoV-2 have not been described yet in intensive care patients, although they are likely involved in Covid-19 pathogenesis. Methods: We performed an observational study during the peak of pandemic in our intensive care unit, dosing D-dimer, C-reactive protein, troponin T, lactate dehydrogenase, ferritin, serum iron, transferrin, transferrin saturation, transferrin soluble receptor, lymphocyte count and NK, CD3, CD4, CD8 and B subgroups of 31 patients during the first 2 weeks of their ICU stay. Correlation with mortality and severity at the time of admission was tested with the Spearman coefficient and Mann–Whitney test. Trends over time were tested with the Kruskal–Wallis analysis. Results: Lymphopenia is severe and constant, with a nadir on day 2 of ICU stay (median 0.555 109/L; interquartile range (IQR) 0.450 109/L); all lymphocytic subgroups are dramatically reduced in critically ill patients, while CD4/CD8 ratio remains normal. Neither ferritin nor lymphocyte count follows significant trends in ICU patients. Transferrin saturation is extremely reduced at ICU admission (median 9%; IQR 7%), then significantly increases at days 3 to 6 (median 33%, IQR 26.5%, p value 0.026). The same trend is observed with serum iron levels (median 25.5 μg/L, IQR 69 μg/L at admission; median 73 μg/L, IQR 56 μg/L on days 3 to 6) without reaching statistical significance. Hyperferritinemia is constant during intensive care stay: however, its dosage might be helpful in individuating patients developing haemophagocytic lymphohistiocytosis. D-dimer is elevated and progressively increases from admission (median 1319 μg/L; IQR 1285 μg/L) to days 3 to 6 (median 6820 μg/L; IQR 6619 μg/L), despite not reaching significant results. We describe trends of all the abovementioned parameters during ICU stay. Conclusions: The description of iron metabolism and lymphocyte count in Covid-19 patients admitted to the intensive care unit provided with this paper might allow a wider understanding of SARS-CoV-2 pathophysiology. [ABSTRACT FROM AUTHOR]

Since December 2019, the world is potentially facing one of the most difficult infectious situations of the last decades. COVID-19 epidemic warrants consideration as a mass casualty incident (MCI) of the highest nature. An optimal MCI/disaster management should consider all four phases of the so-called disaster cycle: mitigation, planning, response, and recovery. COVID-19 outbreak has demonstrated the worldwide unpreparedness to face a global MCI. This present paper thus represents a call for action to solicitate governments and the Global Community to actively start effective plans to promote and improve MCI management preparedness in general, and with an obvious current focus on COVID-19. [ABSTRACT FROM AUTHOR]

Background: An epidemic may exhibit different spatial patterns with a change in geographic scale, with each scale having different conduits and impediments to disease spread. Mapping disease at each of these scales often reveals different cluster patterns. This paper will consider this change of geographic scale in an analysis of yellow fever deaths for New Orleans in 1878. Global clustering for the whole city, will be followed by a focus on the French Quarter, then clusters of that area, and finally street-level patterns of a single cluster. The three-dimensional visualization capabilities of a GIS will be used as part of a cluster creation process that incorporates physical buildings in calculating mortality-to-mortality distance. Including nativity of the deceased will also capture cultural connection. Results: Twenty-two yellow fever clusters were identified for the French Quarter. These generally mirror the results of other global cluster and density surfaces created for the entire epidemic in New Orleans. However, the addition of building-distance, and disease specific time frame between deaths reveal that disease spread contains a cultural component. Same nativity mortality clusters emerge in a similar time frame irrespective of proximity. Italian nativity mortalities were far more densely grouped than any of the other cohorts. A final examination of mortalities for one of the nativity clusters reveals that further sub-division is present, and that this pattern would only be revealed at this scale (street level) of investigation. Conclusion: Disease spread in an epidemic is complex resulting from a combination of geographic distance, geographic distance with specific connection to the built environment, disease-specific time frame between deaths, impediments such as herd immunity, and social or cultural connection. This research has shown that the importance of cultural connection may be more important than simple proximity, which in turn might mean traditional quarantine measures should be re-evaluated. [ABSTRACT FROM AUTHOR]

Background: The seasonality of cholera is described in various study areas throughout the world. However, no study examines how temporal cycles of the disease vary around the world or reviews its hypothesized causes. This paper reviews the literature on the seasonality of cholera and describes its temporal cycles by compiling and analyzing 32 years of global cholera data. This paper also provides a detailed literature review on regional patterns and environmental and climatic drivers of cholera patterns. Data, Methods, and Results: Cholera data are compiled from 1974 to 2005 from the World Health Organization Weekly Epidemiological Reports, a database that includes all reported cholera cases in 140 countries. The data are analyzed to measure whether season, latitude, and their interaction are significantly associated with the country-level number of outbreaks in each of the 12 preceding months using separate negative binomial regression models for northern, southern, and combined hemispheres. Likelihood ratios tests are used to determine the model of best fit. The results suggest that cholera outbreaks demonstrate seasonal patterns in higher absolute latitudes, but closer to the equator, cholera outbreaks do not follow a clear seasonal pattern. Conclusion: The findings suggest that environmental and climatic factors partially control the temporal variability of cholera. These results also indirectly contribute to the growing debate about the effects of climate change and global warming. As climate change threatens to increase global temperature, resulting rises in sea levels and temperatures may influence the temporal fluctuations of cholera, potentially increasing the frequency and duration of cholera outbreaks. [ABSTRACT FROM AUTHOR]

Despite the fact that diabetes and other non-communicable diseases represent a significant proportion of the global burden of disease, proportionate global action has not occurred. A 2003 article reported on global constraints to the implementation of effective policies to curb non-communicable disease epidemics. These constraints include a lack of global advocacy, insufficient attention from funding agencies and governments, partnerships and interactions, capacity and resources, and global norms and standards, as well as orientation of health services to acute care. Building on these ideas, this paper will review the progress that has been made with regards to each constraint, focusing on the International Diabetes Federation's Unite for Diabetes campaign and United Nations resolution on diabetes to show how this event -- driven by globalization -- has helped remove some of these barriers. Additional progress in diabetes and NCD prevention and control is also highlighted. The paper concludes by outlining what still needs to happen for globalization to be an effective solution for diabetes and non-communicable disease prevention and control. [ABSTRACT FROM AUTHOR]

West Nile virus (WNV) represents a serious burden to human and animal health because of its capacity to cause unforeseen and large epidemics. Until 2004, only lineage 1 and 3 WNV strains had been found in Europe. Lineage 2 strains were initially isolated in 2004 (Hungary) and in 2008 (Austria) and for the first time caused a major WNV epidemic in 2010 in Greece with 262 clinical human cases and 35 fatalities. Since then, WNV lineage 2 outbreaks have been reported in several European countries including Italy, Serbia and Greece. Understanding the interaction of ecological factors that affect WNV transmission is crucial for preventing or decreasing the impact of future epidemics. The synchronous co-occurrence of competent mosquito vectors, virus, bird reservoir hosts, and susceptible humans is necessary for the initiation and propagation of an epidemic. Weather is the key abiotic factor influencing the life-cycles of the mosquito vector, the virus, the reservoir hosts and the interactions between them. The purpose of this paper is to review and compare mosquito population dynamics, and weather conditions, in three ecologically different contexts (urban/semi-urban, rural/agricultural, natural) across four European countries (Italy, France, Serbia, Greece) with a history of WNV outbreaks. Local control strategies will be described as well. Improving our understanding of WNV ecology is a prerequisite step for appraising and optimizing vector control strategies in Europe with the ultimate goal to minimize the probability of WNV infection. [ABSTRACT FROM AUTHOR]

Dengue, an acute febrile disease transmitted by Aedes mosquitoes, is caused by the dengue virus (DENV), presenting a formidable challenge to global public health. By examining clues from ancient Chinese books and conducting a comprehensive review, this study elucidates the characteristics of potential dengue epidemics in China prior to 1978. This evidence indicates that China may not have experience dengue epidemics before 1840. During 1840–1949, however, it experienced a noticeable dengue occurrence and prevalence in the 1870s, 1920s, and 1940s. Then from 1949 to 1978, only sporadic reports were accounted. The disparity in the frequency of dengue occurrences across three time periods suggests that the persistent characteristic of dengue epidemics in China primarily arises from imported cases resulting from international exchanges, subsequently leading to local outbreaks influenced by global epidemic trend. This research offers a novel perspective on retrospectively examining the historical trajectory of dengue epidemics and provides valuable insights into exploration of DENV epidemic patterns. [ABSTRACT FROM AUTHOR]

Introduction: Pandemics are global health crises that have caused millions of deaths, enormous socio-economic disruptions, and considerable public and global health security consequences. This study assessed Nigerians' knowledge, attitudes, and perceptions regarding preparedness for the next pandemics. Methods: A cross-sectional questionnaire-based national survey was conducted among 1276 respondents in Nigeria to determine public knowledge, attitudes, and perceptions of preparedness for future pandemics. Descriptive and inferential statistics were used for analyses. Results: Of the 1276 respondents to this survey, > 50% had not heard of the Spanish flu and Zika disease. Approximately half (594) of the respondents did not consider antimicrobial resistance a possible cause of future pandemics. Over an average proportion of respondents reported unsatisfactory knowledge (655, 51.3%) and poor perceptions (695, 54.5%) about pandemic preparedness. The respondents' good knowledge and satisfactory attitudes toward pandemic preparedness decreased with age. The respondents' education, age, and state of residence were significantly associated with good knowledge, satisfactory attitudes, and positive perceptions of pandemic preparedness with respondents with tertiary education levels being at least two times more likely to have these positive outcomes. Conclusion: This study revealed serious gaps in Nigerians' knowledge, attitudes, and perceptions of their readiness for upcoming pandemics. To protect Nigerians' health, emphasis should be placed on these issues by all stakeholders, including the government. [ABSTRACT FROM AUTHOR]

Objective: Since January 8, 2023, China has managed COVID-19 as a Class-B infectious disease, marking the epidemic's transition to a low-level stage. This study analyzes the relationship between the public's perceived a community with shared future for doctor-patient (PCSF), health self-consciousness, benefit finding, and anxiety in this stage. Additionally, it compares changes in these variables across different stages of COVID-19. Methods: Using a repeated cross-sectional design, three surveys were conducted respectively in three different stages of COVID-19 in China. Specifically, the first survey was conducted in Beijing, Dalian, Zhengzhou, Heihe, and Shangrao from November 13 to 20, 2021 in the outbreak stage of COVID-19, yielding 1,252 valid responses out of 1,534 collected questionnaires. The second survey was conducted in Dalian, Zhengzhou, Heihe, Shangrao, and Lanzhou from December 1 to 19, 2021 in the stable stage of COVID-19, with 872 valid responses obtained from 1,075 collected questionnaires. The third survey was conducted in Beijing, Dalian, Zhengzhou, Heihe, Shangrao, Lanzhou, and Chengdu from January 29 to February 4, 2023 in the low epidemic level stage of COVID-19, achieving 2,113 valid responses from the 2,461 questionnaires collected. Results: Unlike in the outbreak stage but similar to the stable stage, the public's anxiety, health self-consciousness and benefit finding decreased while PCSF was improved in the low epidemic level stage. Consistent with both the outbreak and stable stage, PCSF, health self-consciousness, benefit finding, and anxiety showed positive correlations in the low epidemic level stage, with health self-consciousness partially mediating the positive impact of PCSF on benefit finding. Unlike in the stable stage but similar to the outbreak stage, anxiety did not moderate the relationship between PCSF and health self-consciousness in the low epidemic level stage. Conclusions: The public's health self-consciousness, benefit finding, and anxiety decreased, while PCSF increased in the low epidemic level stage. Furthermore, PCSF had a greater impact on benefit finding, and anxiety's impact on health self-consciousness was significantly reduced. Across different stages of COVID-19, PCSF directly increased benefit finding and also enhanced benefit finding by improving health self-consciousness. Thus, comprehensive intervention measures are beneficial in the low epidemic level stage. [ABSTRACT FROM AUTHOR]

Background: Visualization plays an important role in epidemic time series analysis and forecasting. Viewing time series data plotted on a graph can help researchers identify anomalies and unexpected trends that could be overlooked if the data were reviewed in tabular form; these details can influence a researcher's recommended course of action or choice of simulation models. However, there are challenges in reviewing data sets from multiple data sources – data can be aggregated in different ways (e.g., incidence vs. cumulative), measure different criteria (e.g., infection counts, hospitalizations, and deaths), or represent different geographical scales (e.g., nation, HHS Regions, or states), which can make a direct comparison between time series difficult. In the face of an emerging epidemic, the ability to visualize time series from various sources and organizations and to reconcile these datasets based on different criteria could be key in developing accurate forecasts and identifying effective interventions. Many tools have been developed for visualizing temporal data; however, none yet supports all the functionality needed for easy collaborative visualization and analysis of epidemic data. Results: In this paper, we present EpiViewer, a time series exploration dashboard where users can upload epidemiological time series data from a variety of sources and compare, organize, and track how data evolves as an epidemic progresses. EpiViewer provides an easy-to-use web interface for visualizing temporal datasets either as line charts or bar charts. The application provides enhanced features for visual analysis, such as hierarchical categorization, zooming, and filtering, to enable detailed inspection and comparison of multiple time series on a single canvas. Finally, EpiViewer provides several built-in statistical Epi-features to help users interpret the epidemiological curves. Conclusion: EpiViewer is a single page web application that provides a framework for exploring, comparing, and organizing temporal datasets. It offers a variety of features for convenient filtering and analysis of epicurves based on meta-attribute tagging. EpiViewer also provides a platform for sharing data between groups for better comparison and analysis. Our user study demonstrated that EpiViewer is easy to use and fills a particular niche in the toolspace for visualization and exploration of epidemiological data. [ABSTRACT FROM AUTHOR]

Background: Describing how and why an evidence-based intervention is adapted for a new population and setting using a formal evaluation and an adaptation framework can inform others seeking to modify evidence-based weight management interventions for different populations or settings. The Working for You intervention was adapted, to fit a workplace environment, from Be Fit Be Well, an evidence-based intervention that targets weight-control and hypertension in patients at an outpatient clinic. Workplace-based efforts that promote diet and activity behavior change among low-income employees have potential to address the obesity epidemic. This paper aims to explicitly describe how Be Fit Be Well was adapted for this new setting and population.Methods: To describe and understand the worksite culture, environment, and policies that support or constrain healthy eating and activity in the target population, we used qualitative and quantitative methods including key informant interviews, focus groups, and a worker survey; these data informed intervention adaptation. We organized the adaptations made to Be Fit Be Well using an adaptation framework from implementation science.Results: The adapted intervention, Working for You, maintains the theoretical premise and evidence-base underpinning Be Fit Be Well. However, it was modified in terms of the means of delivery (i.e., rather than using interactive voice response, Working for You employs automated SMS text messaging), defined as a modification to context by the adaptation framework. The adaptation framework also includes modifications to content; in this case the behavioral goals were modified for the target population based on updated science related to weight loss and to target a workplace population (e.g., a goal to avoiding free food at work).Conclusions: If effective, this scalable and relatively inexpensive intervention can be translated to other work settings to reduce obesity and diabetes risk among low-SES workers, a group with a higher prevalence of these conditions. Using a formal evaluation and framework to guide and organize how and why an evidence-based intervention is adapted for a new population and setting can push the field of intervention research forward.Trial Registration: ClinicalTrials.gov: NCT02934113 ; Received: October 12, 2016; Updated: November 7, 2017. [ABSTRACT FROM AUTHOR]

Background: Over the last years, substantial effort has been put into enhancing our arsenal in fighting epidemics from both technological and theoretical perspectives with scientists from different fields teaming up for rapid assessment of potentially urgent situations. This paper focusses on the computational aspects of infectious disease models and applies commonly available graphics processing units (GPUs) for the simulation of these models. However, fully utilizing the resources of both CPUs and GPUs requires a carefully balanced heterogeneous approach. Results: The contribution of this paper is twofold. First, an efficient GPU implementation for evaluating a small-scale ODE model; here, the basic S(usceptible)-I(nfected)-R(ecovered) model, is discussed. Second, an asynchronous particle swarm optimization (PSO) implementation is proposed where batches of particles are sent asynchronously from the host (CPU) to the GPU for evaluation. The ultimate goal is to infer model parameters that enable the model to correctly describe observed data. The particles of the PSO algorithm are candidate parameters of the model; finding the right one is a matter of optimizing the likelihood function which quantifies how well the model describes the observed data. By employing a heterogeneous approach, in which both CPU and GPU are kept busy with useful work, speedups of 10 to 12 times can be achieved on a moderate machine with a high-end consumer GPU as compared to a high-end system with 32 CPU cores. Conclusions: Utilizing GPUs for parameter inference can bring considerable increases in performance using average host systems with high-end consumer GPUs. Future studies should evaluate the benefit of using newer CPU and GPU architectures as well as applying this method to more complex epidemiological scenarios. [ABSTRACT FROM AUTHOR]

Background: There have been numerous outbreaks of infectious diseases to date, and out of them, six events have been declared as Public Health Emergencies of International Concern by the World Health Organization (WHO). The outbreaks have been unprecedented, and the sudden anxiety associated with these makes the situation more complicated. Prioritisation of diseases has been a common process in public health, and it is important to identify infectious diseases that are important for human health. In addition, a One Health approach requires taking also animal origin and related risk factors for infectious diseases into account. Methods: State-level co-creation workshops were conducted in Gujarat, Rajasthan, and Maharashtra, the three western states of India. One Health Risk and Disease (OHRAD) prioritisation tool was used to prioritise emerging and epidemic-prone diseases and their risk factors. Results: The risk factors and diseases were scored using the OHRAD tool, and the ranks were obtained. Avian flu by A(H5N1), A(H7N9), and A(H9N2), swine flu by A(H1N1), A(H1N2) and A(H3N2), dengue, rabies, leptospirosis, bovine tuberculosis, salmonellosis, chikungunya, soil-transmitted helminths, and brucellosis were some of the 10 diseases prioritised in all the three western states of the country. Similarly, the presence of mosquito breeding sites near houses or workplaces, open defecation, poor hand hygiene, poor personal hygiene, reluctance to follow disease prevention measures and improper carcass disposal were prioritised as the risk factors for diseases in all three states. Conclusion: The emerging and epidemic-prone diseases and risk factors that are prioritised reflect state-specific scenarios and the gravity of the situation concerning risk factors for disease outbreaks. This can help in allocating resources and increasing efforts in the prevention and control of these diseases. [ABSTRACT FROM AUTHOR]

Background: Dengue fever (DF) has emerged as a significant public health concern in China. The spatiotemporal patterns and underlying influencing its spread, however, remain elusive. This study aims to identify the factors driving these variations and to assess the city-level risk of DF epidemics in China. Methods: We analyzed the frequency, intensity, and distribution of DF cases in China from 2003 to 2022 and evaluated 11 natural and socioeconomic factors as potential drivers. Using the random forest (RF) model, we assessed the contributions of these factors to local DF epidemics and predicted the corresponding city-level risk. Results: Between 2003 and 2022, there was a notable correlation between local and imported DF epidemics in case numbers (r = 0.41, P < 0.01) and affected cities (r = 0.79, P < 0.01). With the increase in the frequency and intensity of imported epidemics, local epidemics have become more severe. Their occurrence has increased from five to eight months per year, with case numbers spanning from 14 to 6641 per month. The spatial distribution of city-level DF epidemics aligns with the geographical divisions defined by the Huhuanyong Line (Hu Line) and Qin Mountain-Huai River Line (Q-H Line) and matched well with the city-level time windows for either mosquito vector activity (83.59%) or DF transmission (95.74%). The RF models achieved a high performance (AUC = 0.92) when considering the time windows. Importantly, they identified imported cases as the primary influencing factor, contributing significantly (24.82%) to local DF epidemics at the city level in the eastern region of the Hu Line (E–H region). Moreover, imported cases were found to have a linear promoting impact on local epidemics, while five climatic and six socioeconomic factors exhibited nonlinear effects (promoting or inhibiting) with varying inflection values. Additionally, this model demonstrated outstanding accuracy (hitting ratio = 95.56%) in predicting the city-level risks of local epidemics in China. Conclusions: China is experiencing an increasing occurrence of sporadic local DF epidemics driven by an unavoidably higher frequency and intensity of imported DF epidemics. This research offers valuable insights for health authorities to strengthen their intervention capabilities against this disease. [ABSTRACT FROM AUTHOR]

SARS-CoV-2, the cause of the COVID-19 pandemic, has introduced a challenging era characterized by the persistent emergence of subvariants. Even after the World Health Organization announced the end of the pandemic, the virus continues to evolve, posing significant challenges to public health responses. This comprehensive review examines the multifaceted impacts of these subvariants, emphasizing their significance across diverse dimensions. SARS-CoV-2 has genetic variability, especially at the spike protein region, which has given rise to Variants of Concern, including Beta, Delta, Gamma, Alpha, and the highly mutable Omicron, which differently exhibit varying levels of immune evasion, disease severity, and transmissibility. Subvariants within the Omicron lineage, including BA.1, BA.2, BA.3, and others, further complicate the landscape with distinct genetic signatures and varying infectivity levels. The impacts extend to diagnostic techniques, treatment strategies, and vaccine effectiveness, underscoring the need for a comprehensive public health response emphasizing preventive measures, genomic surveillance, and vaccination campaigns. Sustaining these interventions is critical, necessitating long-term strategies considering socio-political factors, community involvement, continuous adaptation of healthcare approaches, robust monitoring, and sustainable public health interventions to effectively combat the virus's ever-changing landscape. [ABSTRACT FROM AUTHOR]

Background: Geographic variables play an important role in the study of epidemics. The role of one such variable, population density, in the spread of influenza is controversial. Prior studies have tested for such a role using arbitrary thresholds for population density above or below which places are hypothesized to have higher or lower mortality. The results of such studies are mixed. The objective of this study is to estimate, rather than assume, a threshold level of population density that separates low-density regions from high-density regions on the basis of population loss during an influenza pandemic. We study the case of the influenza pandemic of 1918-19 in India, where over 15 million people died in the short span of less than one year. Methods: Using data from six censuses for 199 districts of India (n=1194), the country with the largest number of deaths from the influenza of 1918-19, we use a sample-splitting method embedded within a population growth model that explicitly quantifies population loss from the pandemic to estimate a threshold level of population density that separates low-density districts from high-density districts. Results: The results demonstrate a threshold level of population density of 175 people per square mile. A concurrent finding is that districts on the low side of the threshold experienced rates of population loss (3.72%) that were lower than districts on the high side of the threshold (4.69%). Conclusions: This paper introduces a useful analytic tool to the health geographic literature. It illustrates an application of the tool to demonstrate that it can be useful for pandemic awareness and preparedness efforts. Specifically, it estimates a level of population density above which policies to socially distance, redistribute or quarantine populations are likely to be more effective than they are for areas with population densities that lie below the threshold. [ABSTRACT FROM AUTHOR]

Background: To understand how infectious agents disseminate throughout a population it is essential to capture the social model in a realistic manner. This paper presents a novel approach to modeling the propagation of the influenza virus throughout a realistic interconnection network based on actual individual interactions which we extract from online social networks. The advantage is that these networks can be extracted from existing sources which faithfully record interactions between people in their natural environment. We additionally allow modeling the characteristics of each individual as well as customizing his daily interaction patterns by making them timedependent. Our purpose is to understand how the infection spreads depending on the structure of the contact network and the individuals who introduce the infection in the population. This would help public health authorities to respond more efficiently to epidemics. Results: We implement a scalable, fully distributed simulator and validate the epidemic model by comparing the simulation results against the data in the 2004-2005 New York State Department of Health Report (NYSDOH), with similar temporal distribution results for the number of infected individuals. We analyze the impact of different types of connection models on the virus propagation. Lastly, we analyze and compare the effects of adopting several different vaccination policies, some of them based on individual characteristics -such as age- while others targeting the super-connectors in the social model. Conclusions: This paper presents an approach to modeling the propagation of the influenza virus via a realistic social model based on actual individual interactions extracted from online social networks. We implemented a scalable, fully distributed simulator and we analyzed both the dissemination of the infection and the effect of different vaccination policies on the progress of the epidemics. The epidemic values predicted by our simulator match real data from NYSDOH. Our results show that our simulator can be a useful tool in understanding the differences in the evolution of an epidemic within populations with different characteristics and can provide guidance with regard to which, and how many, individuals should be vaccinated to slow down the virus propagation and reduce the number of infections. [ABSTRACT FROM AUTHOR]

Background: This exploratory paper outlines an epidemic simulator built on an agent-based, data-driven model of the spread of a disease within an urban environment. An intent of the model is to provide insight into how a disease may reach a tipping point, spreading to an epidemic of uncontrollable proportions. Methods: As a complement to analytical methods, simulation is arguably an effective means of gaining a better understanding of system-level disease dynamics within a population and offers greater utility in its modeling capabilities. Our investigation is based on this conjecture, supported by data-driven models that are reasonable, realistic and practical, in an attempt to demonstrate their efficacy in studying system-wide epidemic phenomena. An agent-based model (ABM) offers considerable flexibility in extending the study of the phenomena before, during and after an outbreak or catastrophe. Results: An agent-based model was developed based on a paradigm of a 'discrete-space scheduled walker' (DSSW), modeling a medium-sized North American City of 650,000 discrete agents, built upon a conceptual framework of statistical reasoning (law of large numbers, statistical mechanics) as well as a correct-by-construction bias. The model addresses where, who, when and what elements, corresponding to network topography and agent characteristics, behaviours, and interactions upon that topography. The DSSW-ABM has an interface and associated scripts that allow for a variety of what-if scenarios modeling disease spread throughout the population, and for data to be collected and displayed via a web browser. Conclusion: This exploratory paper also presents several research opportunities for exploiting data sources of a non-obvious and disparate nature for the purposes of epidemic modeling. There is an increasing amount and variety of data that will continue to contribute to the accuracy of agent-based models and improve their utility in modeling disease spread. The model developed here is well suited to diseases where there is not a predisposition for contraction within the population. One of the advantages of agent-based modeling is the ability to set up a rare event and develop policy as to how one may mitigate damages arising from it. [ABSTRACT FROM AUTHOR]

Background: In 2007 Australia experienced its first outbreak of highly infectious equine influenza. Government disease control measures were put in place to control, contain, and eradicate the disease; these measures included movement restrictions and quarantining of properties. This study was conducted to assess the psycho-social impacts of this disease, and this paper reports the prevalence of, and factors influencing, psychological distress during this outbreak. Methods: Data were collected using an online survey, with a link directed to the affected population via a number of industry groups. Psychological distress, as determined by the Kessler 10 Psychological Distress Scale, was the main outcome measure. Results: In total, 2760 people participated in this study. Extremely high levels of non-specific psychological distress were reported by respondents in this study, with 34% reporting high psychological distress (K10 > 22), compared to levels of around 12% in the Australian general population. Analysis, using backward stepwise binary logistic regression analysis, revealed that those living in high risk infection (red) zones (OR = 2.00; 95% CI: 1.57-2.55; p < 0.001) and disease buffer (amber) zones (OR = 1.83; 95% CI: 1.36-2.46; p < 0.001) were at much greater risk of high psychological distress than those living in uninfected (white zones). Although prevalence of high psychological distress was greater in infected EI zones and States, elevated levels of psychological distress were experienced in horse-owners nationally. Statistical analysis indicated that certain groups were more vulnerable to high psychological distress; specifically younger people, and those with lower levels of formal educational qualifications. Respondents whose principal source of income was from horse-related industry were more than twice as likely to have high psychological distress than those whose primary source of income was not linked to horse-related industry (OR = 2.23; 95% CI: 1.82-2.73; p < 0.001). Conclusion: Although, methodologically, this study had good internal validity, it has limited generalisability because it was not possible to identify, bound, or sample the target population accurately. However, this study is the first to collect psychological distress data from an affected population during such a disease outbreak and has potential to inform those involved in assessing the potential psychological impacts of human infectious diseases, such as pandemic influenza. [ABSTRACT FROM AUTHOR]

Background: The Cuban HIV/AIDS epidemic has the lowest prevalence rate of the Caribbean region. The objective of this paper is to give an overview of the HIV/AIDS epidemic in Cuba and to explore the reasons for this low prevalence. Methods: Data were obtained from the Cuban HIV/AIDS programme established in 1983. This programme has an extensive adult HIV testing policy, including testing of all pregnant women. HIV and AIDS cases have been recorded since 1986. Persons found to be HIV-positive are interviewed on their sexual behaviour and partners. Tracing and voluntary testing of these partners are organised. Epidemiological description of this epidemic was obtained from analysis of this data set. Using elementary mathematical analyses, we estimated the coverage of the detection system (percentage of HIV-positive adults detected) and the average period between HIV infection and detection. Estimated HIV prevalence rates were corrected to account for the coverage. Results: HIV prevalence has increased since 1996. In 2005, the prevalence among pregnant women was 1.2 per 10,000 (16/137000). Estimated HIV prevalence among 15- to 49-year-olds was 8.1 per 10,000 (4913/6065000; 95%CI: 7.9 per 10,000 - 8.3 per 10,000). Most (77%) of the HIV-positive adults were men, most (85.1%) of the detected HIV-positive men were reported as having sex with men (MSM), and most of the HIV-positive women reported having had sex with MSM. The average period between HIV infection and detection was estimated to be 2.1 years (IQR = 1.7 - 2.2 years). We estimated that, for the year 2005, 79.6% (IQR: 77.3 - 81.4%) of the HIVpositive persons were detected. Conclusion: MSM drive the HIV epidemic in Cuba. The extensive HIV testing policy may be an important factor in explaining the low HIV prevalence. To reduce the HIV epidemic in Cuba, the epidemic among MSM should be addressed. To understand this epidemic further, data on sexual behaviour should be collected. Now that antiretroviral therapy is more widely available, the Cuban policy, based on intensive HIV testing and tracing of partners, may be considered as a possible policy to control HIV/AIDS epidemics in other countries. [ABSTRACT FROM AUTHOR]

Background: Global resource needs estimation is a critical part of addressing the HIV/AIDS epidemic. To generate these estimates knowledge of costs and cost structures is required. The evidence base for costs of HIV prevention programmes is limited. Even less is known about the existence of economies scale and whether, as economic theory suggests, average costs form a 'u'-shaped curve as scale increases. Using an econometric analysis, this paper addresses this question by estimating marginal costs and economies of scale for HIV prevention programmes for vulnerable groups in Southern India with different levels of coverage. Methods: Two hybrid translog-cost functions were estimated. First, expenditure data from 78 state-funded HIV prevention projects in Andhra Pradesh were used to explore the impact of scale, institutional history and price on costs; second, economic cost data from 16 commercial sex worker projects across Tamil Nadu and Andhra Pradesh were analysed to additionally assess the impact of the value of inputs not reported in expenditure data and location. Coefficient estimates were used to calculate marginal costs and economies of scale. Results: The econometric model yielded a good fit (R² = 0.46, p < 0.001 and R² = 0.79, p < 0.001, for the expenditure and economic cost datasets, respectively). The economies of scale index was greater than 1 for both datasets and fell as coverage increased. Analysis of the expenditure data found economies of scale were not exhausted, with a 0.002% change in total cost for each extra person reached and an 11% difference in total cost between target group categories. Estimation using the economic cost data suggests a point of minimum efficient scale at around 1750-2000 people reached, a 0.03% change in total cost for each extra person reached, and 28% lower costs in Tamil Nadu than Andhra Pradesh. Conclusion: Econometric analysis of these standardized datasets provides insights into how costs change with coverage, the impact of project location and nature of the project target group. The results demonstrate the importance of understanding the nature of the cost function when designing, budgeting and estimating resource requirements for scaling up coverage of HIV prevention projects. [ABSTRACT FROM AUTHOR]

Background: The accuracy of predicting disease occurrence using epidemic models relies on an understanding of the system or population under investigation. At the time of the Foot and Mouth disease (FMD) outbreak of 2001, there were limited reports in the literature as to the cattle population structure in Britain. In this paper we examine the temporal patterns of cattle births, deaths, imports and movements occurring within Britain, reported to the Department for the Environment, Food and Rural Affairs (DEFRA) through the British Cattle Movement service (BCMS) during the period 1st January 2002 to 28th February 2005. Results: In Britain, the number of reported cattle births exhibit strong seasonality characterised by a large spring peak followed by a smaller autumn peak. Other event types also exhibit strong seasonal trends; both the reported number of cattle slaughtered and "on-farm" cattle deaths increase during the final part of the year. After allowing for seasonal components by smoothing the data, we illustrate that there is very little remaining non-seasonal trend in the number of cattle births, "on-farm" deaths, slaughterhouse deaths, on- and off-movements. However after allowing for seasonal fluctuations the number of cattle imports has been decreasing since 2002. Reporting of movements, births and deaths was more frequent on certain days of the week. For instance, greater numbers of cattle were slaughtered on Tuesdays, Wednesdays and Thursdays. Evidence for digit preference was found in the reporting of births and "on-farm" deaths with particular bias towards over reporting on the 1st, 10th and 20th of each month. Conclusion: This study provides insight into the population and movement dynamics of the British cattle population. Although the population is in constant flux, seasonal and long term trends can be identified in the number of reported births, deaths and movements of cattle. Incorporating this temporal variation in epidemic disease modelling may result in more accurate model predictions and may usefully inform future surveillance strategies. [ABSTRACT FROM AUTHOR]

Background: The Estimations and Projections Package (EPP 2005) for HIV/AIDS estimates and projects HIV prevalence, number of people living with HIV and new HIV infections and AIDS cases using antenatal clinic (ANC) surveillance data. The prevalence projection produced by EPP can be transferred to SPECTRUM, a demographic projection model, to calculate the number of AIDS deaths. This paper presents estimates and projections of HIV prevalence, new cases of HIV infections and AIDS deaths in Tanzania between 2001 and 2010 using the EPP 2005 and SPECTRUM soft-wares on ANC data. Methods: For this study we used; the 1985 - 2004 ANC data set, the 2005 UN population estimates for urban and rural adults, which is based on the 2002 population census, and results of the 2003 Tanzania HIV Indicator Survey. The ANC surveillance sites were categorized into urban and rural areas on the basis of the standard national definitions of urban and rural areas, which led to 40 urban and 35 rural clinic sites. The rural and urban epidemics were run independently by fitting the model to all data and on level fits. Results: The national HIV prevalence increased from 0% in 1981 to a peak of 8.1% in 1995, and gradually decreased to 6.5% in 2004 which stabilized until 2010. The urban HIV epidemic increased from 0% in 1981 peaking at 12.6% in 1992 and leveled to between 10.9% and 11.8% from 2003 to 2010. The rural epidemic peaked in 1995 at 7.0% and gradually declined to 5.2% in 2004, and then stabilized at between 5.1% and 5.3% from 2005 to 2010. New infections are projected to rise steadily, resulting in 250,000 new cases in 2010. Deaths due to AIDS started in 1985 and rose steadily to reach 120,000 deaths in 2010, with more females dying than men. Conclusion: The fact that the number of new infections is projected to increase steadily to reach 250,000 per year in 2010 calls for more concerted efforts to combat the spread of HIV infection particularly in the rural areas where the infrastructure needed for prevention programmes such as counseling and testing, condom accessibility and AIDS information is less developed. [ABSTRACT FROM AUTHOR]

Background: The HIV epidemic among men who have sex with men (MSM) continues to expand globally. The addition of an efficacious, prophylactic vaccine to combination prevention offers immense hope, particularly in low- and middle- income countries which bear the greatest global impact. However, in these settings, there is a paucity of vaccine preparedness studies that specifically pertain to MSM. Our study is the first vaccine preparedness study among MSM and female sex workers (FSWs) in Kenya. In this paper, we explore willingness of Kenyan MSM to participate in HIV vaccine efficacy trials. In addition to individual and socio-cultural motivators and barriers that influence willingness to participate (WTP), we explore the associations or linkages that participants draw between their experiences with or knowledge of medical research both generally and within the context of HIV/AIDS, their perceptions of a future HIV vaccine and their willingness to participate in HIV vaccine trials.Methods: Using a social network-based approach, we employed snowball sampling to recruit MSM into the study from Kisumu, Mombasa, and Nairobi. A field team consisting of seven community researchers conducted in-depth interviews with a total of 70 study participants. A coding scheme for transcribed and translated data was developed and the data was then analysed thematically.Results: Most participants felt that an HIV vaccine would bring a number of benefits to self, as well as to MSM communities, including quelling personal fears related to HIV acquisition and reducing/eliminating stigma and discrimination shouldered by their community. Willingness to participate in HIV vaccine efficacy trials was highly motivated by various forms of altruism. Specific researcher responsibilities centred on safe-guarding the rights and well-being of participants were also found to govern WTP, as were reflections on the acceptability of a future preventive HIV vaccine.Conclusion: Strategies for engagement of communities and recruitment of trial volunteers for HIV vaccine efficacy trials should not only be grounded in and informed by investigations into individual and socio-cultural factors that impact WTP, but also by explorations of participants' existing experiences with or knowledge of medical research as well as attitudes and acceptance towards a future HIV vaccine. [ABSTRACT FROM AUTHOR]

Background: In many low and middle income countries (LMICs), the distribution of adulthood nutritional imbalance is shifting from a predominance of undernutrition to overnutrition. This complex problem poses a huge challenge to governments, non-state actors, and individuals desirous of addressing the problem of malnutrition in LMICs. The objective of this study was to systematically review the literature towards providing an estimate of the prevalence of overweight and obesity among adult Ghanaians. Methods: This study followed the recommendations outlined in the PRISMA statement. Searches were performed in PubMed, Science Direct, google scholar, Africa Journals Online (AJOL) and the WHO African Index Medicus database. This retrieved studies (published up to 31st March 2016) that reported overweight and obesity prevalence among Ghanaians. All online searches were supplemented by reference screening of retrieved papers to identify additional studies. Results: Forty-three (43) studies involving a total population of 48,966 sampled across all the ten (10) regions of Ghana were selected for the review. Our analysis indicates that nearly 43% of Ghanaian adults are either overweight or obese. The national prevalence of overweight and obesity were estimated as 25.4% (95% CI 22.2-28.7%) and 17.1% (95% CI = 14.7-19.5%), respectively. Higher prevalence of overweight (27.2% vs 16.7%) and obesity (20.6% vs 8.0%) were estimated for urban than rural dwellers. Prevalence of overweight (27.8% vs 21.8%) and obesity (21.9% vs 6.0%) were also significantly higher in women than men. About 45.6% of adult diabetes patients in Ghana are either overweight or obese. At the regional level, about 43.4%, 36.9%, 32.4% and 55.2% of residents in Ashanti, Central, Northern and Greater Accra region, respectively are overweight or obese. These patterns generally mimic the levels of urbanization. Per studies' publication years, consistent increases in overweight and obesity prevalence were observed in Ghana in the period 1998-2016. Conclusions: There is a high and rising prevalence of overweight and obesity among Ghanaian adults. The possible implications on current and future population health, burden of chronic diseases, health care spending and broader economy could be enormous for a country still battling many infectious and parasitic diseases. Public health preventive measures that are appropriate for the Ghanaian context, culturally sensitive, cost-effective and sustainable are urgently needed to tackle this epidemic. [ABSTRACT FROM AUTHOR]

Background: For the purpose of epidemiological surveillance, the Hospital University Institute Méditerranée infection has implemented since 2013 a system named MIDaS, based on the systematic collection of routine activity materials, including MALDI-TOF spectra, and results. The objective of this paper is to present the pipeline we use for processing MALDI-TOF spectra during epidemiological surveillance in order to disclose proteinic cues that may suggest the existence of epidemic processes in complement of incidence surveillance. It is illustrated by the analysis of an alarm observed for Streptococcus pneumoniae.Methods: The MALDI-TOF spectra analysis process looks for the existence of clusters of spectra characterized by a double time and proteinic close proximity. This process relies on several specific methods aiming at contrasting and clustering the spectra, presenting graphically the results for an easy epidemiological interpretation, and for determining the discriminating spectra peaks with their possible identification using reference databases.Results: The use of this pipeline in the case of an alarm issued for Streptococcus pneumoniae has made it possible to reveal a cluster of spectra with close proteinic and temporal distances, characterized by the presence of three discriminant peaks (5228.8, 5917.8, and 8974.3 m/z) and the absence of peak 4996.9 m/z. A further investigation on UniProt KB showed that peak 5228.8 is possibly an OxaA protein and that the absent peak may be a transposase.Conclusion: This example shows this pipeline may support a quasi-real time identification and characterization of clusters that provide essential information on a potentially epidemic situation. It brings valuable information for epidemiological sensemaking and for deciding on the continuation of the epidemiological investigation, in particular the involving of additional costly resources to confirm or invalidate the alarm.Clinical Trials Registration: NCT03626987. [ABSTRACT FROM AUTHOR]