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Spread of the Anopheles stephensi mosquito, an invasive malaria vector, threatens to put an additional 126 million persons per year in Africa at risk for malaria. To accelerate the early detection and rapid response to this mosquito species, confirming its presence and geographic extent is critical. However, existing molecular species assays require specialized laboratory equipment, interpretation, and sequencing confirmation. We developed and optimized a colorimetric rapid loop-mediated isothermal amplification assay for molecular An. stephensi species identification. The assay requires only a heat source and reagents and can be used with or without DNA extraction, resulting in positive color change in 30–35 minutes. We validated the assay against existing PCR techniques and found 100% specificity and analytical sensitivity down to 0.0003 ng of genomic DNA. The assay can successfully amplify single mosquito legs. Initial testing on samples from Marsabit, Kenya, illustrate its potential as an early vector detection and malaria mitigation tool.

Evidence on mortality rates and causes of death associated with extrapulmonary nontuberculous mycobacteria (NTM) infection is limited. This nationwide register-based study in Denmark used diagnostic codes to match adult patients with extrapulmonary NTM infection 1:4 to controls. During 2000–2017, we identified 485 patients, who had significantly more comorbidities than controls. The 5-year mortality rate for patients was 26.8% (95% CI 23.1%–31.0%) and for controls, 10.9% (95% CI 9.6%–12.4%). The median age at death was 76 (interquartile range 63–85) years for patients and 84 (interquartile range 73–90) years for controls. The adjusted hazard rate of death for patients was 1.34 (95% CI 1.10–1.63; p = 0.004). Patients and controls mainly died of cardiovascular disease and solid malignant neoplasms. Hematologic malignancies and HIV were more frequently causes of death in patients. Mortality rates are substantial among patients with extrapulmonary NTM infection, predominantly caused by underlying conditions.

Measles in persons with secondary vaccination failure (SVF) may be less infectious than cases in unvaccinated persons. Our systematic review aimed to assess transmission risk for measles after SVF. We searched PubMed, Embase, and Web of Science databases from their inception dates. Inclusion criteria were articles describing persons who were exposed to measles-infected persons who had experienced SVF. Across the included 14 studies, >3,030 persons were exposed to measles virus from SVF cases, of whom 180 were susceptible, indicating secondary attack rates of 0%–6.25%. We identified 109 cases of SVF from the studies; 10.09% (n = 11) of case-patients transmitted the virus, resulting in 23 further cases and yielding an effective reproduction number of 0.063 (95% CI 0.0–0.5). These findings suggest a remarkably low attack rate for SVF measles cases, suggesting that, In outbreak situations, public health management of unvaccinated persons could be prioritized over persons with SVF.

Nontuberculous mycobacteria (NTM) are emerging as notable causative agents of opportunistic infections. To examine clinical significance, species distribution, and temporal trends of NTM in Denmark, we performed a nationwide register-based study of all unique persons with NTM isolated in the country during 1991–2022. We categorized patients as having definite disease, possible disease, or isolation by using a previously validated method. The incidence of pulmonary NTM increased throughout the study period, in contrast to earlier findings. Mycobacterium malmoense, M. kansasii, M. szulgai, and M. avium complex were the most clinically significant species based on microbiologic findings; M. avium dominated in incidence. This study shows the need for surveillance for an emerging infection that is not notifiable in most countries, provides evidence to support clinical decision-making, and highlights the importance of not considering NTM as a single entity.

We conducted a prospective study of 159 cases of Streptococcus dysgalactiae subspecies equisimilis (SDSE) bacteremia in 157 patients at 2 hospitals in Finland during November 2015–November 2019. Cellulitis was associated with nonsevere disease (p = 0.008); necrotizing fasciitis was associated with severe disease (p = 0.004). Fifty percent of patients had >1 clinical characteristic associated with risk for death. The case-fatality rate was 6%, and 7% of patients were treated in an intensive care unit. Blood leukocyte counts on days 2 (p = 0.032) and 3 (p = 0.020) and C-reactive protein levels on days 3 (p = 0.030) and 4 (p = 0.009) after admission were predictors of severe disease. The Pitt bacteremia score was an accurate predictor of death. Using the Pitt bacteremia score, leukocyte counts, and CRP responses during initial treatment can improve treatment strategies and survival for patients with SDSE.

Nematodes of the Onchocercidae family, such as Pelecitus spp., are filarial parasites of medical and veterinary importance. Although infections are widely distributed among avian species, only 2 cases of human Pelecitus ocular infection, both in South America, have been reported. We describe a 61-year-old man in northeast Thailand diagnosed with an ocular infection. Morphologic characteristics suggested the causative agent was a female Pelecitus nematode: coiled body, rounded anterior and posterior extremities, a distinct preesophageal cuticular ring, lateral alae, a postdeirid, and a protuberant vulva. Sequences of the 12S rDNA gene indicated 95%–96% identity and cox1 gene 92%–96% identity with published P. copsychi sequences. P-distance for cox1 sequences between the causative agent and P. copsychi was 6.71%. Phylogenetic trees of 12S rDNA and cox1 genes indicated the species differed from but is closely associated with P. copsychi. Healthcare providers should be aware of the threat of ocular infection from Pelecitus spp. nematodes.

To investigate epidemiology of and risk factors for laboratory-confirmed mpox during the 2022 outbreak in Nigeria, we enrolled 265 persons with suspected mpox. A total of 163 (61.5%) were confirmed to have mpox; 137 (84.0%) were adults, 112 (68.7%) male, 143 (87.7%) urban/semi-urban dwellers, 12 (7.4%) self-reported gay men, and 3 (1.8%) female sex workers. Significant risk factors for adults were sexual and nonsexual contact with persons who had mpox, as well as risky sexual behavior. For children, risk factors were close contact with an mpox-positive person and prior animal exposure. Odds of being mpox positive were higher for adults with HIV and lower for those co-infected with varicella zoster virus (VZV). No children were HIV-seropositive; odds of being mpox positive were higher for children with VZV infection. Our findings indicate mpox affects primarily adults in Nigeria, partially driven by sexual activity; childhood cases were driven by close contact, animal exposure, and VZV co-infection.

During 2018–2021, eight septic transfusion reactions occurred from transfusion of platelet units contaminated with Acinetobacter spp., Staphylococcus saprophyticus, Leclercia adecarboxylata, or a combination of those environmental organisms. Whether biofilm formation contributed to evasion of bacterial risk mitigations, including bacterial culture, point-of-care testing, or pathogen-reduction technology, is unclear. We designed a 12-well plate-based method to evaluate environmental determinants of single-species and multispecies biofilm formation in platelets. We evaluated bacteria isolated from septic transfusion reactions for biofilm formation by using crystal violet staining and enumeration of adherent bacteria. Most combinations of bacteria had enhanced biofilm production compared with single bacteria. Combinations involving L. adecarboxylata had increased crystal violet biofilm production and adherent bacteria. This study demonstrates that transfusion-relevant bacteria can produce biofilms well together. More work is needed to clarify the effect of biofilms on platelet bacterial risk control strategies, but US Food and Drug Administration–recommended strategies remain acceptable.

Nontuberculous mycobacterial pulmonary disease (NTM-PD) prevalence is a rising public health concern. We assessed the long-term healthcare systems perspective of costs incurred by 147 NTM-PD patients at a tertiary hospital in South Korea. Median cumulative total medical cost in managing NTM-PD patients was US $5,044 (interquartile range US $3,586–$9,680) over 49.7 months (interquartile range 33.0–68.2 months) of follow-up. The major cost drivers were diagnostic testing and medication, accounting for 59.6% of total costs. Higher costs were associated with hospitalization for Mycobacterium abscessus infection and pulmonary comorbidities. Of the total medical care costs, 50.2% were patient co-payments resulting from limited national health insurance coverage. As South Korea faces significant problems of poverty during old age and increasing NTM-PD prevalence, the financial and socio-economic burden of NTM-PD may become a major public health concern that should be considered with regard to adequate strategies for NTM-PD patients.

Western equine encephalitis virus (WEEV) is a mosquitoborne virus that reemerged in December 2023 in Argentina and Uruguay, causing a major outbreak. We investigated the outbreak using epidemiologic, entomological, and genomic analyses, focusing on WEEV circulation near the Argentina‒Uruguay border in Rio Grande do Sul state, Brazil. During November 2023‒April 2024, the outbreak in Argentina and Uruguay resulted in 217 human cases, 12 of which were fatal, and 2,548 equine cases. We determined cases on the basis of laboratory and clinical epidemiologic criteria. We characterized 3 fatal equine cases caused by a novel WEEV lineage identified through a nearly complete coding sequence analysis, which we propose as lineage C. Our findings highlight the importance of continued surveillance and equine vaccination to control future WEEV outbreaks in South America.

Formal infectious disease surveillance in Ukraine has been disrupted by Russia’s 2022 invasion, leading to challenges with tracking and containing epidemics. To analyze the effects of the war on infectious disease epidemiology, we used open-source data from EPIWATCH, an artificial intelligence early-warning system. We analyzed patterns of infectious diseases and syndromes before (November 1, 2021–February 23, 2022) and during (February 24–July 31, 2022) the conflict. We compared case numbers for the most frequently reported diseases with numbers from formal sources and found increases in overall infectious disease reports and in case numbers of cholera, botulism, tuberculosis, HIV/AIDS, rabies, and salmonellosis during compared with before the invasion. During the conflict, although open-source intelligence captured case numbers for epidemics, such data (except for diphtheria) were unavailable/underestimated by formal surveillance. In the absence of formal surveillance during military conflicts, open-source data provide epidemic intelligence useful for infectious disease control.

Plague is a deadly zoonosis that still poses a threat in many regions of the world. We combined epidemiologic, host, and vector surveillance data collected during 1961–1980 from the Araripe Plateau focus in northeastern Brazil with ecologic, geoclimatic, and Yersinia pestis genomic information to elucidate how these factors interplay in plague activity. We identified well-delimited plague hotspots showing elevated plague risk in low-altitude areas near the foothills of the plateau’s concave sectors. Those locations exhibited distinct precipitation and vegetation coverage patterns compared with the surrounding areas. We noted a seasonal effect on plague activity, and human cases linearly correlated with precipitation and rodent and flea Y. pestis positivity rates. Genomic characterization of Y. pestis strains revealed a foundational strain capable of evolving into distinct genetic variants, each linked to temporally and spatially constrained plague outbreaks. These data could identify risk areas and improve surveillance in other plague foci within the Caatinga biome.

We conducted a longitudinal cohort study of SARS-CoV-2 and influenza rates in childcare centers and schools in Wuxi, China, collecting 1,760 environmental samples and 9,214 throat swabs from 593 students (regardless of symptoms) in weekly collections during February–June 2023. We estimated a cumulative infection rate of 124.8 (74 episodes)/1,000 persons for SARS-CoV-2 and 128.2 (76 episodes)/1,000 persons for influenza. The highest SARS-CoV-2 infection rate was in persons 18 years of age, and for influenza, in children 4 years of age. The asymptomatic proportion of SARS-CoV-2 was 59.6% and 66.7% for influenza; SARS-CoV-2 symptomatic proportion was lower in 16–18-year-olds than in 4–6-year-olds. Only samples from frequently touched surface tested positive for SARS-CoV-2 (4/1,052) and influenza (1/1,052). We found asynchronous circulation patterns of SARS-CoV-2 and influenza, similar to trends in national sentinel surveillance. The results support vaccination among pediatric populations and other interventions, such as environmental disinfection in educational settings.

Malaria is increasingly diagnosed in urban centers across the Amazon Basin. In this study, we combined repeated prevalence surveys over a 4-year period of a household-based random sample of 2,774 persons with parasite genotyping to investigate the epidemiology of malaria in Mâncio Lima, the main urban transmission hotspot in Amazonian Brazil. We found that most malarial infections were asymptomatic and undetected by point-of-care microscopy. Our findings indicate that as malaria transmission decreases, the detection threshold of microscopy rises, resulting in more missed infections despite similar parasite densities estimated by molecular methods. We identified genetically highly diverse populations of Plasmodium vivax and P. falciparum in the region; occasional shared lineages between urban and rural residents suggest cross-boundary propagation. The prevalence of low-density and asymptomatic infections poses a significant challenge for routine surveillance and the effectiveness of malaria control and elimination strategies in urbanized areas with readily accessible laboratory facilities.

The 2022 global mpox outbreak was driven by human-to-human transmission, but modes of transmission by sexual relationship versus sexual contact remain unclear. We evaluated sexual transmission of mpox by using monkeypox virus (MPXV) G2R-mRNA as a marker of ongoing viral replication through in vitro experiments. We analyzed clinical samples of 15 MPXV-positive patients in Italy from different biological regions by using the setup method. The presence of MPXV DNA, MPXV G2R-mRNA, or both in all analyzed lesion swab samples, independent of viral load, confirmed a higher infectivity risk from skin lesions. Positivity for MPXV G2R-mRNA in nasopharyngeal swabs was associated with high MPXV load, whereas positive results for MPXV G2R-mRNA were obtained only in the 2 semen samples with the lowest MPXV loads. Our results suggest that close or skin-to-skin contact during sexual intercourse is the main route of sexual transmission and that semen is a minor driver of infection, regardless of MPXV load.

Cutaneous leishmaniasis is atypical in Sri Lanka because Leishmania donovani, which typically causes visceral disease, is the causative agent. The origins of recently described hybrids between L. donovani and other Leishmania spp. usually responsible for cutaneous leishmaniasis remain unknown. Other endemic dermotropic Leishmania spp. have not been reported in Sri Lanka. Genome analysis of 27 clinical isolates from Sri Lanka and 32 Old World Leishmania spp. strains found 8 patient isolates clustered with L. tropica and 19 with L. donovani. The L. tropica isolates from Sri Lanka shared markers with strain LtK26 reported decades ago in India, indicating they were not products of recent interspecies hybridization. Because L. tropica was isolated from patients with leishmaniasis in Sri Lanka, our findings indicate L. donovani is not the only cause of cutaneous leishmaniasis in Sri Lanka and potentially explains a haplotype that led to interspecies dermotropic L. donovani hybrids.

Since 2022, Europe has had 4 cases of extensively drug-resistant Neisseria gonorrhoeae, sequence type 16406, that is resistant to ceftriaxone and highly resistant to azithromycin. We report 2 new cases from France in 2023 involving strains genetically related to the 4 cases from Europe as well as isolates from Cambodia.

We assessed the effect of rotavirus vaccination coverage on the number of inpatients with gastroenteritis of all ages in Japan. We identified patients admitted with all-cause gastroenteritis during 2011–2019 using data from the Diagnosis Procedure Combination system in Japan. We used generalized estimating equations with a Poisson distribution, using hospital codes as a cluster variable to estimate the impact of rotavirus vaccination coverage by prefecture on monthly numbers of inpatients with all-cause gastroenteritis. We analyzed 294,108 hospitalizations across 569 hospitals. Higher rotavirus vaccination coverage was associated with reduced gastroenteritis hospitalizations compared with the reference category of vaccination coverage 80%, adjusted incidence rate ratio was 0.87 [95% CI 0.83–0.90]). Our results show that achieving higher rotavirus vaccination coverage among infants could benefit the entire population by reducing overall hospitalizations for gastroenteritis for all age groups.

An outbreak of influenza A (H5N1) virus was detected in dairy cows in the United States. We detected influenza A virus sialic acid -α2,3/α2,6-galactose host receptors in bovine mammary glands by lectin histochemistry. Our results provide a rationale for the high levels of H5N1 virus in milk from infected cows.

Alphavirus infections are transmitted by mosquitoes, but the mode of transmission for Mycobacterium ulcerans, which causes Buruli ulcer, is contested. Using notification data for Victoria, Australia, during 2017–2022, adjusted for incubation period, we show close alignment between alphavirus and Buruli ulcer seasons, supporting the hypothesis of mosquito transmission of M. ulcerans.

We isolated highly pathogenic avian influenza (HPAI) H5N5 and H5N1 viruses from crows in Hokkaido, Japan, during winter 2023–24. They shared genetic similarity with HPAI H5N5 viruses from northern Europe but differed from those in Asia. Continuous monitoring and rapid information sharing between countries are needed to prevent HPAI virus transmission.

Mansonella ozzardi, a filarioid parasite, causes human mansonellosis in the Americas. We identified raccoons (Procyon lotor) as wildlife reservoirs of M. ozzardi in Costa Rica. Noting the sympatry of free-ranging raccoons and humans, we conclude that mansonellosis is a considerable public health risk in the region.

We investigated a fatal case of primary amoebic meningoencephalitis from an indoor surfing center in Taiwan. The case was detected through encephalitis syndromic surveillance. Of 56 environmental specimens, 1 was positive for Naegleria fowleri ameba. This report emphasizes the risk for N. fowleri infection from inadequately disinfected recreational waters, even indoors.

We used Medicare data to identify >88,000 adults >65 years of age diagnosed and treated for Lyme disease during 2016–2019 in the United States. Most diagnoses occurred among residents of high-incidence states, in summer, and among men. Incidence of diagnoses was substantially higher than that reported through public health surveillance.

We identified a cluster of mpox exposures among key populations in Kenya through retrospective serologic screening. We identified strong seropositivity among sex workers and gay, bisexual, and other men who have sex with men. These findings demonstrate the need for increased mpox surveillance among mpox-endemic and mpox-endemic–adjacent regions in Africa.

Severe babesiosis with 9.8% parasitemia was diagnosed in a patient in the Netherlands who had previously undergone splenectomy. We confirmed Babesia venatorum using PCR and sequencing. B. venatorum was also the most prevalent species in Ixodes ricinus ticks collected around the patient’s home. Our findings warrant awareness for severe babesiosis in similar patients.

Using participatory, virologic, and wastewater surveillance systems, we estimated when and to what extent reported data of adult COVID-19 cases underestimated COVID-19 incidence in Germany. We also examined how case underestimation evolved over time. Our findings highlight how community-based surveillance systems can complement official notification systems for respiratory disease dynamics.

Pneumocystis jirovecii pneumonia is an opportunistic infection that affects HIV-infected and immunocompromised persons and rarely affects immunocompetent patients. However, after the advent of the COVID-19 pandemic, some COVID-19 patients without immunocompromise or HIV were infected with P. jirovecii. Clinical manifestations were atypical, easily misdiagnosed, and rapidly progressive, and the prognosis was poor.

We identified a Thelazia callipaeda eyeworm in an American black bear in Pennsylvania, USA, on the basis of its morphological features and molecular analysis. Our finding highlights emergence of a T. callipaeda worm sylvatic transmission cycle in the United States.

We report a case of Taenia solium taeniasis in a 10-year-old child in Timor-Leste, confirmed by molecular analysis, suggesting T. solium transmission to humans is occurring in Timor-Leste. Proactive measures are needed to improve public understanding of prevalence, geographic spread, and health implications of human taeniasis and cysticercosis in Timor-Leste.

The COVID-19 pandemic, caused by SARS-CoV-2, disproportionately affected certain segments of society, particularly the elderly population (which suffered the brunt of the burden of the pandemic in terms of severity of the disease, hospitalization, and death). This study presents a generalized multigroup model, with m heterogeneous sub-populations, to assess the population-level impact of age heterogeneity and vaccination on the transmission dynamics and control of the SARS-CoV-2 pandemic in the United States. Rigorous analysis of the model for the homogeneous case (i.e., the model with m = 1) reveal that its disease-free equilibrium is globally-asymptotically stable for two special cases (with perfect vaccine efficacy or negligible disease-induced mortality) whenever the associated reproduction number is less than one. The model has a unique and globally-asymptotically stable endemic equilibrium, for special a case, when the associated reproduction threshold exceeds one. The homogeneous model was fitted using the observed cumulative mortality data for the United States during three distinct waves (Waves A (October 17, 2020 to April 5, 2021), B (July 9, 2021 to November 7, 2021) and C (January 1, 2022 to May 7, 2022)) chosen to align with time periods when the Alpha, Delta and Omicron were, respectively, the predominant variants in the United States. The calibrated model was used to derive a theoretical expression for achieving vaccine-derived herd immunity (needed to eliminate the disease in the United States). It was shown that, using the one-group homogeneous model, vaccine-derived herd immunity is not attainable during Wave C of the pandemic in the United States, regardless of the coverage level of the fully-vaccinated individuals. Global sensitivity analysis was carried out to determine the parameters of the model that have the most influence on the disease dynamics and burden. These analyses reveal that control and mitigation strategies that may be very effective during one wave may not be so very effective during the other wave or waves. However, strategies that target asymptomatic and pre-symptomatic infectious individuals are shown to be consistently effective across all waves. To study the impact of the disproportionate effect of COVID-19 on the elderly population, we considered the heterogeneous model for the case where the total population is subdivided into the sub-populations of individuals under 65 years of age and those that are 65 and older. The resulting two-group heterogeneous model, which was also fitted using the cumulative mortality data for wave C, was also rigorously analysed. Unlike for the case of the one-group model, it was shown, for the two-group model, that vaccine-derived herd immunity can indeed be achieved during Wave C of the pandemic if at least 61% of the populace is fully vaccinated. Thus, this study shows that adding age heterogeneity into a SARS-CoV-2 vaccination model with homogeneous mixing significantly reduces the level of vaccination coverage needed to achieve vaccine-derived herd immunity (specifically, for the heterogeneous model, herd-immunity can be attained during Wave C if a moderate proportion of susceptible individuals are fully vaccinated). The consequence of this result is that vaccination models for SARS-CoV-2 that do not explicitly account for age heterogeneity may be overestimating the level of vaccine-derived herd immunity threshold needed to eliminate the SARS-CoV-2 pandemic.

The complex interactions were performed among non-pharmaceutical interventions, vaccinations, and hosts for all epidemics in mainland China during the spread of COVID-19. Specially, the small-scale epidemic in the city described by SVEIR model was less found in the current studies. The SVEIR model with control was established to analyze the dynamical and epidemiological features of two epidemics in Jinzhou City led by Omicron variants before and after Twenty Measures. In this study, the total population (N) of Jinzhou City was divided into five compartments: the susceptible (S), the vaccinated (V), the exposed (E), the infected (I), and the recovered (R). By surveillance data and the SVEIR model, three methods (maximum likelihood method, exponential growth rate method, next generation matrix method) were governed to estimate basic reproduction number, and the results showed that an increasing tendency of basic reproduction number from Omicron BA.5.2 to Omicron BA.2.12.1. Meanwhile, the effective reproduction number for two epidemics were investigated by surveillance data, and the results showed that Jinzhou wave 1 reached the peak on November 1 and was controlled 7 days later, and that Jinzhou wave 2 reached the peak on November 28 and was controlled 5 days later. Moreover, the impacts of non-pharmaceutical interventions (awareness delay, peak delay, control intensity) were discussed extensively, the variations of infection scales for Omicron variant and EG.5 variant were also discussed. Furthermore, the investigations on peaks and infection scales for two epidemics in dynamic zero-COVID policy were operated by the SVEIR model with control. The investigations on public medical requirements of Jinzhou City and Liaoning Province were analyzed by using SVEIR model without control, which provided a possible perspective on variant evolution in the future.

The ongoing transmission of mpox in specific countries and regions necessitates urgent action. It is essential to implement targeted containment strategies that concentrate on high-risk populations and critical locations, such as college campuses, to effectively curb the spread of mpox. This study is dedicated to evaluating the performance of various vaccination and quarantine strategies in curbing the spread of mpox and estimating the outbreak risk. To accomplish this, we constructed a stochastic, agent-based, discrete-time susceptible-latent-infectious-recovered (SLIR) model, to examine mpox transmission on a simulated college campus. Our findings reveal that relying solely on PEP is insufficient in containing mpox effectively. To bolster the population immunity and protect the vulnerable, pre-exposure vaccination among high-risk populations prior to an outbreak is imperative. Our study demonstrates that a pre-exposure vaccination rate of 50% in high-risk populations can led to a remarkable 74.2% reduction of infections. This translated to a mere 1.0% cumulative infection incidence in the overall population. In cases where the desired vaccination coverage is not attainable, enhancing case detection and isolation measures can serve as an effective emergency response to contain mpox outbreaks. For pre-exposure vaccination coverage of 20% or lower, a 40% isolation ratio is necessary to keep the cumulative number of infections in check. However, when the coverage exceeds 30%, a reduced isolation ratio of 20% becomes sufficient to manage the outbreak effectively. These insights underscore the importance of strategic pre-exposure vaccination in conjunction with robust surveillance and isolation protocols to safeguard public health and prevent the escalation of mpox outbreaks.

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA—GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA—GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models’ predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA—GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA—GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Parameter identification involves the estimation of undisclosed parameters within a system based on observed data and mathematical models. In this investigation, we employ DAISY to meticulously examine the structural identifiability of parameters of a within-host SARS-CoV-2 epidemic model, taking into account an array of observable datasets. Furthermore, Monte Carlo simulations are performed to offer a comprehensive practical analysis of model parameters. Lastly, sensitivity analysis is employed to ascertain that decreasing the replication rate of the SARS-CoV-2 virus and curbing the infectious period are the most efficacious measures in alleviating the dissemination of COVID-19 amongst hosts.

Introduction: Tuberculosis (TB) is one of the most prevalent infectious diseases in the world, causing major public health problems in developing countries. The rate of TB incidence in Iran was estimated to be 13 per 100,000 in 2021. This study aimed to estimate the reproduction number and serial interval for pulmonary tuberculosis in Iran. Material and methods: The present national historical cohort study was conducted from March 2018 to March 2022 based on data from the National Tuberculosis and Leprosy Registration Center of Iran's Ministry of Health and Medical Education (MOHME). The study included 30,762 tuberculosis cases and 16,165 new smear-positive pulmonary tuberculosis patients in Iran. We estimated the reproduction number of pulmonary tuberculosis in a Bayesian framework, which can incorporate uncertainty in estimating it. Statistical analyses were accomplished in R software. Results: The mean age at diagnosis of patients was 52.3 ± 21.2 years, and most patients were in the 35–63 age group (37.1%). Among the data, 9121 (56.4%) cases were males, and 7044 (43.6%) were females. Among patients, 7459 (46.1%) had a delayed diagnosis between 1 and 3 months. Additionally, 3039 (18.8%) cases were non-Iranians, and 2978 (98%) were Afghans. The time-varying reproduction number for pulmonary tuberculosis disease was calculated at an average of 1.06 ± 0.05 (95% Crl 0.96–1.15). Conclusions: In this study, the incidence and the time-varying reproduction number of pulmonary tuberculosis showed the same pattern. The mean of the time-varying reproduction number indicated that each infected person is causing at least one new infection over time, and the chain of transmission is not being disrupted.

We propose a malaria model involving the sensitive and resistant strains, which is described by reaction-diffusion equations. The model reflects the scenario that the vector and host populations disperse with distinct diffusion rates, susceptible individuals or vectors cannot be infected by both strains simultaneously, and the vector population satisfies the logistic growth. Our main purpose is to get a threshold type result on the model, especially the interaction effect of the two strains in the presence of spatial structure. To solve this issue, the basic reproduction number (BRN) R0i and invasion reproduction number (IRN) Rˆ0i of each strain (i = 1 and 2 are for the sensitive and resistant strains, respectively) are defined. Furthermore, we investigate the influence of the diffusion rates of populations and vectors on BRNs and IRNs.

Non-communicable diseases (NCD) are the most important cause of death in the world. The socio-economic costs associated with NCDs makes it imperative to prevent and control them in the 21st century. The severe toll that the COVID-19 pandemic has taken worldwide is an unfortunate illustration of our limited insight into the infectious risk for the global population. Co-incidence between NCD and infection offers an underexplored opportunity to design preventive policies. In a pilot survey, we observed that the NCD population displays a substantial reduction in their social contacting behavior as compared to the general population. This indicates that existing mathematical models based on contact surveys in the general population are not applicable to the NCD population and that the risk of acquiring an infection following a contact is probably underestimated. Our demonstration of reduced social mixing in several chronic conditions, raises the question to what extent the social mixing is influenced by the burden of disease. We advocate the design of disease-specific contact surveys to address how the burden of disease associates with social contact behavior and the risk of infection. The SARS-CoV-2 pandemic offers an unprecedented opportunity to gain insight into the importance of infection in the NCD population and to find ways to improve healthcare procedures.

This paper deals with the problem of the prediction and control of cholera outbreak using real data of Cameroon. We first develop and analyze a deterministic model with seasonality for the cholera, the novelty of which lies in the incorporation of undetected cases. We present the basic properties of the model and compute two explicit threshold parameters R¯0 and R̲0 that bound the effective reproduction number R0, from below and above, that is R̲0≤R0≤R¯0. We prove that cholera tends to disappear when R¯0≤1, while when R̲0>1, cholera persists uniformly within the population. After, assuming that the cholera transmission rates and the proportions of newly symptomatic are unknown, we develop the EnKf approach to estimate unmeasurable state variables and these unknown parameters using real data of cholera from 2014 to 2022 in Cameroon. We use this result to estimate the upper and lower bound of the effective reproduction number and reconstructed active asymptomatic and symptomatic cholera cases in Cameroon, and give a short-term forecasts of cholera in Cameroon until 2024. Numerical simulations show that (i) the transmission rate from free Vibrio cholerae in the environment is more important than the human transmission and begin to be high few week after May and in October, (ii) 90% of newly cholera infected cases that present the symptoms of cholera are not diagnosed and (iii) 60.36% of asymptomatic are detected at 14% and 86% of them recover naturally. The future trends reveals that an outbreak appeared from July to November 2023 with the number of cases reported monthly peaked in October 2023. An impulsive control strategy is incorporated in the model with the aim to avoid or prevent the cholera outbreak. In the first year of monitoring, we observed a reduction of more than 75% of incidences and the disappearance of the peaks when no control are available in Cameroon. A second monitoring of control led to a further reduction of around 60% of incidences the following year, showing how impulse control could be an effective means of eradicating cholera.

We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to the maximum instantaneous growth rate of new infections, echoing concepts and tools developed to study the reactivity of ecosystems. Suitable epidemicity indices can showcase the dynamics of infections, together with commonly employed effective reproduction numbers, especially when the latter assume values less than 1. In particular, epidemicity evaluates the short-term reactivity to perturbations of a disease-free equilibrium. Here, we show that sufficient epidemicity thresholds to prevent transient epidemic outbreaks in a spatially connected setting can be estimated by generalizing existing analogues derived when spatial effects are neglected. We specifically account for the discrete nature, in both space and time, of surveillance data of the type typically employed to estimate effective reproduction numbers that formed the bulk of the communication of the state of the COVID-19 pandemic and its controls. After analyzing the effects of spatial heterogeneity on the considered prognostic indicators, we perform a short- and long-term analysis on the COVID-19 pandemic in Italy, showing that endemic conditions were maintained throughout the duration of our simulation despite stringent control measures. Our method provides a portfolio of prognostic indices that are essential to pinpoint the ongoing pandemic in both a qualitative and quantitative manner, as our results demonstrate. We base our conclusions on extended investigations of the effects of spatial fragmentation of communities of different sizes owing to connectivity by human mobility and contact scenarios, within real geographic contexts and synthetic setups designed to test our framework.

This paper studies an Ebola epidemic model with an exponential nonlinear incidence function that considers the efficacy and the behaviour change. The current model also incorporates a new density-dependent treatment that catches the impact of the disease transmission on the treatment. Firstly, we provide a theoretical study of the nonlinear differential equations model obtained. More precisely, we derive the effective reproduction number and, under suitable conditions, prove the stability of equilibria. Afterwards, we show that the model exhibits the phenomenon of backward-bifurcation whenever the bifurcation parameter and the reproduction number are less than one. We find that the bi-stability and backward-bifurcation are not automatically connected in epidemic models. In fact, when a backward-bifurcation occurs, the disease-free equilibrium may be globally stable. Numerically, we use well-known standard tools to fit the model to the data reported for the 2018–2020 Kivu Ebola outbreak, and perform the sensitivity analysis. To control Ebola epidemics, our findings recommend a combination of a rapid behaviour change and the implementation of a proper treatment strategy with a high level of efficacy. Secondly, we propose and analyze a fractional-order Ebola epidemic model, which is an extension of the first model studied. We use the Caputo operator and construct the Grünwald-Letnikov nonstandard finite difference scheme, and show its advantages.

Respiratory syncytial virus (RSV) poses a significant global health threat, especially affecting infants and the elderly. Addressing this, the present study proposes an innovative approach to vaccine design, utilizing immunoinformatics and computational strategies. We analyzed RSV's structural proteins across both subtypes A and B, identifying potential helper T lymphocyte, cytotoxic T lymphocyte, and linear B lymphocyte epitopes. Criteria such as antigenicity, allergenicity, toxicity, and cytokine-inducing potential were rigorously examined. Additionally, we evaluated the conservancy of these epitopes and their population coverage across various RSV strains. The comprehensive analysis identified six major histocompatibility complex class I (MHC-I) binding, five MHC-II binding, and three B-cell epitopes. These were integrated with suitable linkers and adjuvants to form the vaccine. Further, molecular docking and molecular dynamics simulations demonstrated stable interactions between the vaccine candidate and human Toll-like receptors (TLR4 and TLR5), with a notable preference for TLR4. Immune simulation analysis underscored the vaccine's potential to elicit a strong immune response. This study presents a promising RSV vaccine candidate and offers theoretical support, marking a significant advancement in vaccine development efforts. However, the promising in silico findings need to be further validated through additional in vivo studies.

Vaccine efficacy and its quantification is a crucial concept for the proper design of public health vaccination policies. In this work we proposed a mathematical model to estimate the efficacy of the influenza vaccine in a real-word scenario. In particular, our model is a SEIR-type epidemiological model, which distinguishes vaccinated and unvaccinated populations. Mathematically, its dynamics is governed by a nonlinear system of ordinary differential equations, where the non-linearity arises from the effective contacts between susceptible and infected individuals. Two key aspects of this study is that we use a vaccine distribution over time that is based on real data specific to the elderly people in the Valencian Community and the calibration process takes into account that over one influenza season a specific proportion of the population becomes infected with influenza. To consider the effectiveness of the vaccine, the model incorporates a parameter, the vaccine attenuation factor, which is related with the vaccine efficacy against the influenza virus. With this framework, in order to calibrate the model parameters and to obtain an influenza vaccine efficacy estimation, we considered the 2016–2017 influenza season in the Valencian Community, Spain, using the influenza reported cases of vaccinated and unvaccinated. In order to ensure the model identifiability, we choose to deterministically calibrate the parameters for different scenarios and we find the one with the minimum error in order to determine the vaccine efficacy. The calibration results suggest that the influenza vaccine developed for 2016–2017 influenza season has an efficacy of approximately 76.7%, and that the risk of becoming infected is five times higher for an unvaccinated individual in comparison with a vaccinated one. This estimation partially agrees with some previous studies related to the influenza vaccine. This study presents a new integrated mathematical approach to study the influenza vaccine efficacy and gives further insight into this important public health topic.

Background: The structure of age groups and social contacts of the total population influenced infection scales and hospital-bed requirements, especially influenced severe infections and deaths during the global prevalence of COVID-19. Before the end of the year 2022, Chinese government implemented the national vaccination and had built the herd immunity cross the country, and announced Twenty Measures (November 11) and Ten New Measures (December 7) for further modifications of dynamic zero-COVID polity on the Chinese mainland. With the nation-wide vaccination and modified measures background, Fuzhou COVID-19 large wave (November 19, 2022–February 9, 2023) led by Omicron BA.5.2 variant was recorded and prevailed for three months in Fujian Province. Methods: A multi-age groups susceptible-exposed-infected-hospitalized-recovered (SEIHR) COVID-19 model with social contacts was proposed in this study. The main object was to evaluate the impacts of age groups and social contacts of the total population. The idea of Least Squares method was governed to perform the data fittings of four age groups against the surveillance data from Fujian Provincial Center for Disease Control and Prevention (Fujian CDC). The next generation matrix method was used to compute basic reproduction number for the total population and for the specific age group. The tendencies of effective reproduction number of four age groups were plotted by using the Epiestim R package and the SEIHR model for in-depth discussions. The sensitivity analysis by using sensitivity index and partial rank correlation coefficients values (PRCC values) were operated to reveal the differences of age groups against the main parameters. Results: The main epidemiological features such as basic reproduction number, effective reproduction number and sensitivity analysis were extensively discussed for multi-age groups SEIHR model in this study. Firstly, by using of the next generation matrix method, basic reproduction number R0 of the total population was estimated as 1.57 using parameter values of four age groups of Fuzhou COVID-19 large wave. Given age group k, the values of R0k (age group k to age group k), the values of R0k (an infected of age group k to the total population) and the values of R^0k (an infected of the total population to age group k) were also estimated, in which the explorations of the impacts of age groups revealed that the relationship R0k>R0k>R^0k was valid. Then, the fluctuating tendencies of effective reproduction number Rt were demonstrated by using two approaches (the surveillance data and the SEIHR model) for Fuzhou COVID-19 large wave, during which high-risk group (G4 group) mainly contributed the infection scale due to high susceptibility to infection and high risks to basic diseases. Further, the sensitivity analysis using two approaches (the sensitivity index and the PRCC values) revealed that susceptibility to infection of age groups played the vital roles, while the numerical simulation showed that infection scale varied with the changes of social contacts of age groups. The results of this study claimed that the high-risk group out of the total population was concerned by the local government with the highest susceptibility to infection against COVID-19. Conclusions: This study verified that the partition structure of age groups of the total population, the susceptibility to infection of age groups, the social contacts among age groups were the important contributors of infection scale. The less social contacts and adequate hospital beds for high-risk group were profitable to control the spread of COVID-19. To avoid the emergence of medical runs against new variant in the future, the policymakers from local government were suggested to decline social contacts when hospital beds were limited.

Rocky Mountain spotted fever (RMSF) is a fatal tick-borne zoonotic disease that has emerged as an epidemic in western North America since the turn of the 21st century. Along the US south-western border and across northern Mexico, the brown dog tick, Rhipicephalus sanguineus, is responsible for spreading the disease between dogs and humans. The widespread nature of the disease and the ongoing epidemics contrast with historically sporadic patterns of the disease. Because dogs are amplifying hosts for the Rickettsia rickettsii bacteria, transmission dynamics between dogs and ticks are critical for understanding the epidemic. In this paper, we developed a compartment metapopulation model and used it to explore the dynamics and drivers of RMSF in dogs and brown dog ticks in a theoretical region in western North America. We discovered that there is an extended lag—as much as two years—between introduction of the pathogen to a naïve population and epidemic-level transmission, suggesting that infected ticks could disseminate extensively before disease is detected. A single large city-size population of dogs was sufficient to maintain the disease over a decade and serve as a source for disease in surrounding smaller towns. This model is a novel tool that can be used to identify high risk areas and key intervention points for epidemic RMSF spread by brown dog ticks.

Background: Throughout the SARS-CoV-2 pandemic, policymakers have had to navigate between recommending voluntary behaviour change and policy-driven behaviour change to mitigate the impact of the virus. While individuals will voluntarily engage in self-protective behaviour when there is an increasing infectious disease risk, the extent to which this occurs and its impact on an epidemic is not known. Methods: This paper describes a deterministic disease transmission model exploring the impact of individual avoidance behaviour and policy-mediated avoidance behaviour on epidemic outcomes during the second wave of SARS-CoV-2 infections in Ontario, Canada (September 1, 2020 to February 28, 2021). The model incorporates an information feedback function based on empirically derived behaviour data describing the degree to which avoidance behaviour changed in response to the number of new daily cases COVID-19. Results: Voluntary avoidance behaviour alone was estimated to reduce the final attack rate by 23.1%, the total number of hospitalizations by 26.2%, and cumulative deaths by 27.5% over 6 months compared to a counterfactual scenario in which there were no interventions or avoidance behaviour. A provincial shutdown order issued on December 26, 2020 was estimated to reduce the final attack rate by 66.7%, the total number of hospitalizations by 66.8%, and the total number of deaths by 67.2% compared to the counterfactual scenario. Conclusion: Given the dynamics of SARS-CoV-2 in a pre-vaccine era, individual avoidance behaviour in the absence of government action would have resulted in a moderate reduction in disease however, it would not have been sufficient to entirely mitigate transmission and the associated risk to the population in Ontario. Government action during the second wave of the COVID-19 pandemic in Ontario reduced infections, protected hospital capacity, and saved lives.

The basic reproduction number, R0, is a well-known quantifier of epidemic spread. However, a class of existing methods for estimating R0 from incidence data early in the epidemic can lead to an over-estimation of this quantity. In particular, when fitting deterministic models to estimate the rate of spread, we do not account for the stochastic nature of epidemics and that, given the same system, some outbreaks may lead to epidemics and some may not. Typically, an observed epidemic that we wish to control is a major outbreak. This amounts to implicit selection for major outbreaks which leads to the over-estimation problem. We formally characterised the split between major and minor outbreaks by using Otsu's method which provides us with a working definition. We show that by conditioning a ‘deterministic’ model on major outbreaks, we can more reliably estimate the basic reproduction number from an observed epidemic trajectory.

During the COVID-19 pandemic, over one thousand papers were published on “Susceptible-Exposed-Infectious-Removed” (SEIR) epidemic computational models. The English word “exposed” in its vernacular and public health usage means a state of having been in contact with an infectious individual, but not necessarily infected. In contrast, the term “exposed” in SEIR modeling usage typically stands for a state of already being infected but not yet being infectious to others, a state more properly termed “latently infected.” In public health language, “exposed” means possibly infected, yet in SEIR modeling language, “exposed” means already infected. This paper retraces the conceptual and mathematical origins of this terminological disconnect and concludes that epidemic modelers should consider using the “SLIR” notational short-hand (L for Latent) instead of SEIR.

Although epidemiological surveillance of COVID-19 has been gradually downgraded globally, the transmission of COVID-19 continues. It is critical to quantify the transmission dynamics of COVID-19 using multiple datasets including wastewater virus concentration data. Herein, we propose a comprehensive method for estimating the effective reproduction number using wastewater data. The wastewater virus concentration data, which were collected twice a week, were analyzed using daily COVID-19 incidence data obtained from Takamatsu, Japan between January 2022 and September 2022. We estimated the shedding load distribution (SLD) as a function of time since the date of infection, using a model employing the delay distribution, which is assumed to follow a gamma distribution, multiplied by a scaling factor. We also examined models that accounted for the temporal smoothness of viral load measurement data. The model that smoothed temporal patterns of viral load was the best fit model (WAIC = 2795.8), which yielded a mean estimated distribution of SLD of 3.46 days (95% CrI: 3.01–3.95 days). Using this SLD, we reconstructed the daily incidence, which enabled computation of the effective reproduction number. Using the best fit posterior draws of parameters directly, or as a prior distribution for subsequent analyses, we first used a model that assumed temporal smoothness of viral load concentrations in wastewater, as well as infection counts by date of infection. In the subsequent approach, we examined models that also incorporated weekly reported case counts as a proxy for weekly incidence reporting. Both approaches enabled estimations of the epidemic curve as well as the effective reproduction number from twice-weekly wastewater viral load data. Adding weekly case count data reduced the uncertainty of the effective reproduction number. We conclude that wastewater data are still a valuable source of information for inferring the transmission dynamics of COVID-19, and that inferential performance is enhanced when those data are combined with weekly incidence data.