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Mathematical models have become very influential, especially during the COVID-19 pandemic. Data and code sharing are indispensable for reproducing them, protocol registration may be useful sometimes, and declarations of conflicts of interest (COIs) and of funding are quintessential for transparency. Here, we evaluated these features in publications of infectious disease-related models and assessed whether there were differences before and during the COVID-19 pandemic and for COVID-19 models versus models for other diseases. We analysed all PubMed Central open access publications of infectious disease models published in 2019 and 2021 using previously validated text mining algorithms of transparency indicators. We evaluated 1338 articles: 216 from 2019 and 1122 from 2021 (of which 818 were on COVID-19); almost a six-fold increase in publications within the field. 511 (39.2%) were compartmental models, 337 (25.2%) were time series, 279 (20.9%) were spatiotemporal, 186 (13.9%) were agent-based and 25 (1.9%) contained multiple model types. 288 (21.5%) articles shared code, 332 (24.8%) shared data, 6 (0.4%) were registered, and 1197 (89.5%) and 1109 (82.9%) contained COI and funding statements, respectively. There was no major changes in transparency indicators between 2019 and 2021. COVID-19 articles were less likely to have funding statements and more likely to share code. Further validation was performed by manual assessment of 10% of the articles identified by text mining as fulfilling transparency indicators and of 10% of the articles lacking them. Correcting estimates for validation performance, 26.0% of papers shared code and 41.1% shared data. On manual assessment, 5/6 articles identified as registered had indeed been registered. Of articles containing COI and funding statements, 95.8% disclosed no conflict and 11.7% reported no funding. Transparency in infectious disease modelling is relatively low, especially for data and code sharing. This is concerning, considering the nature of this research and the heightened influence it has acquired. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of the novel coronavirus disease of 2019 (COVID-19) has led to huge disruptions in the medical field and society. The significance of training and education for experts has been increasingly acknowledged in Japan, where the number of infectious disease (ID) specialists is reportedly insufficient. In this paper, we report the results of a web-based survey that was conducted to reveal the ways in which the COVID-19 pandemic has influenced medical students' awareness of ID specialists and future career choices. Method: This cross-sectional descriptive study was conducted in March 2021 and targeted 717 medical students belonging to Medical School of Okayama University, Japan. The questionnaire consisted of four questions meant to assess students' knowledge and future intentions of becoming ID specialists. Results: We obtained 328 eligible questionnaires (response rate: 45.7%). Of 227 (69.2%) students who were aware of ID specialists, 99 (43.6%) answered that they came to know about them only after the pandemic, 12 (3.7%) answered that their interest in being an ID specialist arose during the pandemic, while 36 (11.0%) responded that they would rather not become ID specialists. At the time of the survey, 5 students (1.5%) were aiming to become ID specialists. Conclusion: We observed a very low rate of interest to be an ID specialist among medical students. The experience of the pandemic does not seem to have influenced Japanese medical students to choose ID as a specialty for their careers. Continuous efforts to increase the number of ID specialists are necessary in Japan as a countermeasure against the coming pandemic. [ABSTRACT FROM AUTHOR]

Compartmental models are often used to understand and predict the progression of an infectious disease such as COVID-19. The most basic of these models consider the total population of a region to be closed. Many incorporate human mobility into their transmission dynamics, usually based on static and aggregated data. However, mobility can change dramatically during a global pandemic as seen with COVID-19, making static data unsuitable. Recently, large mobility datasets derived from mobile devices have been used, along with COVID-19 infections data, to better understand the relationship between mobility and COVID-19. However, studies to date have relied on data that represent only a fraction of their target populations, and the data from mobile devices have been used for measuring mobility within the study region, without considering changes to the population as people enter and leave the region. This work presents a unique case study in Andorra, with comprehensive datasets that include telecoms data covering 100% of mobile subscribers in the country, and results from a serology testing program that more than 90% of the population voluntarily participated in. We use the telecoms data to both measure mobility within the country and to provide a real-time census of people entering, leaving and remaining in the country. We develop multiple SEIR (compartmental) models parameterized on these metrics and show how dynamic population metrics can improve the models. We find that total daily trips did not have predictive value in the SEIR models while country entrances did. As a secondary contribution of this work, we show how Andorra's serology testing program was likely impacted by people leaving the country. Overall, this case study suggests how using mobile phone data to measure dynamic population changes could improve studies that rely on more commonly used mobility metrics and the overall understanding of a pandemic. [ABSTRACT FROM AUTHOR]

Many factors play a role in outcomes of an emerging highly contagious disease such as COVID-19. Identification and better understanding of these factors are critical in planning and implementation of effective response strategies during such public health crises. The objective of this study is to examine the impact of factors related to social distancing, human mobility, enforcement strategies, hospital capacity, and testing capacity on COVID-19 outcomes within counties located in District of Columbia as well as the states of Maryland and Virginia. Longitudinal data have been used in the analysis to model county-level COVID-19 infection and mortality rates. These data include big location-based service data, which were collected from anonymized mobile devices and characterize various social distancing and human mobility measures within the study area during the pandemic. The results provide empirical evidence that lower rates of COVID-19 infection and mortality are linked with increased levels of social distancing and reduced levels of travel—particularly by public transit modes. Other preventive strategies and polices also prove to be influential in COVID-19 outcomes. Most notably, lower COVID-19 infection and mortality rates are linked with stricter enforcement policies and more severe penalties for violating stay-at-home orders. Further, policies that allow gradual relaxation of social distancing measures and travel restrictions as well as those requiring usage of a face mask are related to lower rates of COVID-19 infections and deaths. Additionally, increased access to ventilators and Intensive Care Unit (ICU) beds, which represent hospital capacity, are linked with lower COVID-19 mortality rates. On the other hand, gaps in testing capacity are related to higher rates of COVID-19 infection. The results also provide empirical evidence for reports suggesting that certain minority groups such as African Americans and Hispanics are disproportionately affected by the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Background: Coronavirus infectious disease 2019 (COVID-19) has had a great impact on global health, but with relatively few confirmed cases in Taiwan. People in Taiwan showed excellent cooperation with the government for disease prevention and faced social and behavioral changes during this period. This study aimed to investigate people's knowledge of COVID-19, attitudes and practices regarding vaccinations for influenza, pneumococcus and COVID-19. Methods: We conducted a community-based, cross-sectional questionnaire survey from September 2020 to October 2020 among adults in northern Taiwan. The four-part questionnaire included questions on sociodemographic characteristics, knowledge, attitude, and practice toward COVID-19. Results: Among a total of 410 respondents, 58.5% were categorized as having "good knowledge" responding to COVID-19. Among the total respondents, 86.6% were willing to receive influenza or pneumococcal vaccines, and 76% of them acted to receive COVID-19 immunization once the vaccine became available. Compared with the respondents with poor knowledge of COVID-19, those with good knowledge had a more positive attitude toward receiving influenza or pneumococcal immunization (OR 3.26, 95% CI = 1.74–6.12). Conclusions: Participants with good knowledge of COVID-19 had greater intent to receive immunization for influenza or pneumococcal vaccine. The promotion of correct knowledge of both COVID-19 and immunization preparations is necessary. [ABSTRACT FROM AUTHOR]

A novel infectious respiratory disease was recognized in Wuhan (Hubei Province, China) in December 2019. In February 2020, the disease was named "coronavirus disease 2019" (COVID-19). COVID-19 became a pandemic in March 2020, and, since then, different countries have implemented a broad spectrum of policies. Thailand is considered to be among the top countries in handling its first wave of the outbreak—12 January to 31 July 2020. Here, we illustrate how Thailand tackled the COVID-19 outbreak, particularly the effects of public health interventions on the epidemiological spread. This study shows how the available data from the outbreak can be analyzed and visualized to quantify the severity of the outbreak, the effectiveness of the interventions, and the level of risk of allowed activities during an easing of a "lockdown." This study shows how a well-organized governmental apparatus can overcome the havoc caused by a pandemic. [ABSTRACT FROM AUTHOR]

The current outbreak of the coronavirus disease 2019 (COVID-19) is an unprecedented example of how fast an infectious disease can spread around the globe (especially in urban areas) and the enormous impact it causes on public health and socio-economic activities. Despite the recent surge of investigations about different aspects of the COVID-19 pandemic, we still know little about the effects of city size on the propagation of this disease in urban areas. Here we investigate how the number of cases and deaths by COVID-19 scale with the population of Brazilian cities. Our results indicate small towns are proportionally more affected by COVID-19 during the initial spread of the disease, such that the cumulative numbers of cases and deaths per capita initially decrease with population size. However, during the long-term course of the pandemic, this urban advantage vanishes and large cities start to exhibit higher incidence of cases and deaths, such that every 1% rise in population is associated with a 0.14% increase in the number of fatalities per capita after about four months since the first two daily deaths. We argue that these patterns may be related to the existence of proportionally more health infrastructure in the largest cities and a lower proportion of older adults in large urban areas. We also find the initial growth rate of cases and deaths to be higher in large cities; however, these growth rates tend to decrease in large cities and to increase in small ones over time. [ABSTRACT FROM AUTHOR]