Airborne transmission is one of the most efficient routes of respiratory viral spread, posing a significant challenge in controlling major infectious diseases such as COVID-19. In microgravity environments, such as the International Space Station (ISS), this mode of transmission requires heightened vigilance and preventive measures due to the prolonged suspension of virus-laden particles, which increases the risk of infection. Using the COVID Airborne Risk Assessment (CARA) tool, we assess the risk of airborne transmission of respiratory viruses, using SARS-CoV-2 as a case study, in microgravity by simulating the emission, dispersion, and inhalation of virus-laden particles. Our simulations show that the unique conditions of microgravity allow these particles to remain airborne for more extended periods compared to Earth, leading to a 286-fold increase in virus concentration in the air and resulting in nearly twice the probability of infection for a susceptible host. We also evaluated the effectiveness of preventive measures. We found that facemasks could reduce the risk by up to 23%, while continuous HEPA filtration at five air changes per hour proves crucial for managing air quality and minimizing infection risks by reducing airborne virus concentration by 99.79%. To explore potential effects of spaceflight-induced immune suppression on transmission risk, we modeled hypothetical scenarios with increased viral shedding based on herpesvirus reactivation data. An 8-fold increase in viral load (as observed for herpesviruses in space) raised infection probability by 12 percentage points above baseline. Sensitivity analysis with 4-fold and 16-fold increases showed infection risk scales proportionally with viral shedding intensity. Although facemasks and air filtration help mitigate the risk, their effectiveness diminishes when viral load is elevated. Enhancing host immunity through vaccination or other interventions is vital, potentially reducing infection probability by up to 14.17% when combined with HEPA filtration.

[This corrects the article DOI: 10.1016/j.isci.2023.107019.].

Wastewater-based epidemiology (WBE) has been widely used to track SARS-CoV-2 transmission using viral RNA, but its capacity to capture population immunity remains poorly defined. Although antibodies can be recovered from wastewater, the relationship between wastewater antibody signals, individual-level shedding dynamics, and community-wide infection and immunity patterns has not been systematically established. We conducted a three-year longitudinal study (2020-2022) across urban and rural communities in Thailand, covering approximately 18 million people. SARS-CoV-2 viral RNA and anti-SARS-CoV-2 IgG concentrations were quantified in wastewater from diverse facility types. To calibrate wastewater signals, faecal viral RNA and IgG shedding kinetics were characterised in 412 individuals from the same communities. Deconvolution models were applied to infer infection incidence from wastewater measurements, and lagged regression analyses assessed associations with confirmed cases, mortality, and vaccination coverage. Faecal viral RNA shedding peaked early after symptom onset and declined rapidly, whereas anti-SARS-CoV-2 IgG exhibited delayed onset, peaked around 50 days, and persisted for several months. These distinct kinetics produced temporally offset wastewater signals, with RNA-based incidence consistently preceding antibody-based estimates. Prior to vaccine rollout, wastewater incidence was dominated by viral RNA signals, while antibody levels remained low. Following widespread vaccination and successive infection waves, antibody-based incidence increased and gradually converged with RNA-based estimates, particularly in highly vaccinated urban areas. Wastewater antibody levels showed strong temporal autocorrelation and positive lagged associations with confirmed cases and vaccination, with weaker associations with mortality. Integrating viral RNA and antibody measurements in wastewater, informed by individual-level shedding dynamics, enables concurrent assessment of infection burden and population immunity.

Accurate infectious-disease forecasts are essential for timely public health decision-making. In this study, we develop a hybrid modeling framework that combines compartmental models with Long Short-Term Memory (LSTM) networks to estimate a key time-varying epidemiological parameter as a case study for leptospirosis in Thailand. Our framework uses an LSTM-ODE model trained on environmental covariates (rainfall, flooding, and temperature) and infected human cases to infer the transmission rate, which shows strong seasonal and environmental dependencies. The results demonstrate that including flooding, temperature, and human cases improves the prediction of infected individuals (MSE = 35.41). Our findings suggest that the integrated hybrid framework offers a more precise solution by improving the estimation of a key epidemiological parameter. The model accommodates multiple input features and, once trained, enables inference suitable for forecasting. Its ability to generate predictions using environmental covariates, particularly when epidemiological surveillance data are incomplete or delayed.

BACKGROUND:Dengue fever remains a critical public health challenge in Thailand, with transmission dynamics driven by complex interactions between environmental and socioeconomic factors. Understanding these predictive factors is essential for developing robust forecasting systems. METHODS:We developed a machine learning framework to classify spatiotemporal dengue risk and identify key predictive factors across Thailand. We analyzed 20 years of monthly dengue hemorrhagic fever surveillance data (2003-2022) from 77 provinces, integrating 54 environmental, climatic, and socioeconomic features. We benchmarked four candidate classifiers - logistic regression, support vector machines, random forests, and eXtreme Gradient Boosting (XGBoost) - and selected XGBoost on the basis of performance across six metrics. SHapley Additive exPlanations (SHAP) were used to interpret feature contributions. The dataset was stratified into training (2003-2016) and testing periods, with the latter subdivided into pre-COVID-19 (2017-2019), COVID-19 (2020-2021), and post-COVID-19 (2022) phases. RESULTS:The XGBoost model achieved an AUC of 0.80 in pre-pandemic testing and 0.74 across the combined during- and post-pandemic period. Temperature dominated the feature-importance ranking, comprising seven of the top ten features, with non-linear thresholds near 21°C for 1-month lagged minimum temperature and near 32°C for 3-month lagged maximum temperature - values that align with established biological constraints on Aedes aegypti-mediated transmission. Precipitation features contributed minimally to model predictions, while a higher Gross Provincial Product was associated with increased dengue risk, consistent with predominantly urban transmission patterns. Model performance deteriorated significantly during the COVID-19 pandemic (AUC = 0.62 in 2021), with systematic overprediction indicating that non-environmental factors operating outside the model dominated dengue dynamics during this period. CONCLUSIONS:Temperature is the dominant predictor of dengue risk in Thailand, and the thresholds we recover correspond closely to known biological constraints on vector competence. Environmentally driven prediction is reliable under stationary conditions but degrades substantially during periods of major societal disruption, underscoring the need to integrate behavioral and surveillance-coverage indicators alongside environmental predictors when applying such models in real time.

Abstract Background Dengue remains a major public health challenge in Thailand despite decades of vector control implementation. While mathematical models have explored dengue transmission dynamics, systematic evaluation of current control strategies under realistic operational conditions remains limited. Methods We developed a temperature-dependent, multi-serotype dengue transmission model that explicitly incorporates three primary vector control strategies: reduction in mosquito biting rates through personal protection measures, further reduction in mosquito birth rates beyond current larval control efforts, and further increase in adult mosquito mortality beyond current adulticide application levels. Using Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC), we fitted the model to dengue hemorrhagic fever (DHF) surveillance data from nine province-year combinations representing high (Rayong), moderate (Ratchaburi), and low (Phrae) transmission settings across three years (2006, 2015, and 2017). The model accounts for four dengue serotypes, temperature-dependent mosquito dynamics, and temporary cross-protective immunity between serotypes. Results The model closely reproduced observed monthly DHF case counts across all nine province-year combinations. Estimated reporting proportions ranged from 1.4% to 16.7%, with the highest values occurring in high-transmission provinces during the 2015 outbreak year. When each strategy was independently intensified by 50% relative to fitted baseline levels, reducing mosquito biting rates and increasing adult mosquito mortality consistently produced greater reductions in transmission than reducing mosquito birth rates. In the highest-transmission scenario (Rayong, 2015), a 50% reduction in biting rate from the baseline level yielded a 96.4% reduction in cumulative infections (95% CrI: 95.4-97.3%), compared with 94.3% (95% CrI: 91.8-95.6%) for a 50% increase in adult mosquito mortality and 77.0% (95% CrI: 58.6-84.6%) for a 50% reduction in mosquito birth rate. Analysis of the time-varying reproduction number ( R t ) confirmed that interventions targeting adult mosquito-human contact achieved the greatest sustained epidemic suppression, although the relative ranking between bite prevention and adulticide application varied by epidemiological setting. Conclusions Under the uniform 50% intensification scenario tested, interventions that directly disrupt adult mosquito-human contact, whether through personal protection or adulticide application, substantially outperformed larval control in reducing dengue transmission across diverse Thai settings. These findings support prioritizing personal protection and adulticide application, while the generalizability of this ranking to other intensification levels and settings warrants further investigation.

Leptospirosis poses a significant public health challenge in Thailand, driven by a complex mix of environmental and socioeconomic factors. This study develops an XGBoost machine learning model to predict leptospirosis outbreak risk at the provincial level in Thailand, integrating climatic, socioeconomic, and agricultural features. Using national surveillance data from 2007-2022, the model was trained to classify provinces as high or low risk based on the median incidence rate. The model's predictive performance was validated for the years 2018-2022, spanning pre-COVID-19, COVID-19, and post-COVID-19 periods. SHapley Additive exPlanation (SHAP) analysis was employed to identify key predictive factors. The optimized XGBoost model achieved high predictive accuracy for the pre-pandemic (AUC = 0.937 with 95% CI: 0.878 - 0.976) and post-pandemic (AUC = 0.951 with 95% CI: 0.861 - 0.999) testing periods. SHAP analysis revealed rice production factors, household size, and specific climatic variables as the strongest predictors of leptospirosis risk. However, model performance declined during the COVID-19 pandemic (2020-2021), suggesting surveillance disruption and potential underreporting. This study demonstrates the utility of machine learning for predicting leptospirosis risk in Thailand and highlights the complex interplay of environmental and socioeconomic factors in driving outbreaks. The adaptable modeling framework provides a foundation for developing early warning systems and targeted interventions to reduce the burden of this neglected tropical disease.

Community-led wastewater surveillance reveals widespread circulation of the mpox virus and clade diversity in conflict-affected countries. This low-cost approach fills critical surveillance gaps, uncovering regional clade variations, including the more severe Clade Ib, and highlighting the potential of decentralized genomic monitoring where traditional systems fail.

Avian influenza viruses (AIVs) pose a growing global health threat, particularly in low- and middle-income countries (LMICs), where limited surveillance capacity and under-resourced healthcare systems hinder timely detection and response. Migratory birds play a significant role in the transboundary spread of AIVs, yet data from key regions along migratory flyways remain sparse. To address these surveillance gaps, we conducted a study between December 2021 and February 2023 using fresh bird guano collected across 10 countries in the Global South. Here, we show that remote, uninhabited regions in previously unsampled areas harbor a high diversity of AIV strains, with H5N1 emerging as the most prevalent. Some of these H5N1 samples also carry mutations that may make them less responsive to the antiviral drug oseltamivir. Our findings documented the presence of AIVs in several underrepresented regions and highlighted critical transmission hotspots where viral evolution may be accelerating. These results underscore the urgent need for geographically targeted surveillance to detect emerging variants, inform public health interventions, and reduce the risk of zoonotic spillover.

BACKGROUND: The COVID-19 pandemic has highlighted the crucial role of testing in mitigating disease transmission. This study evaluates the effectiveness and cost-efficiency of various testing strategies, including daily screening, symptom-based testing, and contact-based testing, using RT-PCR, RT-LAMP, and antigen tests. METHODS: We employed stochastic modeling on a contact network to assess the impact of these strategies on outbreak control. Simulations were conducted to evaluate the probability of an outbreak, epidemic size, and testing costs for each strategy. Scenarios with varying levels of population immunity were also explored. RESULTS: Daily screening, particularly with RT-PCR and RT-LAMP, significantly reduced transmission risks but incurred higher costs. Symptom-based testing offered a more cost-effective alternative, albeit with lower efficacy in mitigating outbreaks. Antigen tests, despite their lower sensitivity, proved to be a cost-effective option for symptom-based testing. Turnaround time of symptom-based testing was a more critical factor than assay sensitivity in containing outbreaks. Combining symptom-based testing with contact tracing further reduced outbreak probability. In scenarios with pre-existing population immunity, testing all symptomatic individuals was the most effective and cost-efficient approach when compared to testing a lower proportion of symptomatic individuals. CONCLUSIONS: Our findings suggest testing strategies could be adapted based on the stage of the epidemic, population immunity, and available resources. Daily screening is most effective but costly, while symptom-based testing combined with contact tracing offers a more cost-effective approach. Antigen tests can be a viable alternative for symptom-based testing in resource-limited settings. Rapid case identification and isolation are crucial for optimal outbreak control. These findings provide valuable insights for designing targeted interventions to protect communities while managing limited resources during current and future infectious disease outbreaks.

Polymer-based microneedles have the potential to improve transdermal drug and vaccine delivery. However, their mechanical strength remains a critical challenge. In this study, we developed a 2D finite element model to investigate the polystyrene (PS) microneedle insertion into a hyperelastic bilayer representing skin. The model treated the microneedles as deformable bodies, enabling observation of microneedle-skin interactions and potential microneedle failure modes, particularly under applicator misalignment and skin curvature conditions. The effects of microneedle geometry, array interspacing, and material properties were also examined. We found that misalignment insertion at an angle of just 5degrees can cause the stresses at the microneedle base to exceed the yield strength of PS, indicating high sensitivity to off-axis loading. The curvature of the skin plays a vital role in non-uniform contact between the microneedle tips and the skin surface, resulting in a higher bending force acting on the outer microneedles. Microneedle shape and tip angle significantly influenced penetration force, while tip diameter notably affected insertion force. Furthermore, the model showed that not all polymers can overcome skin resistance forces, even when made into the same microneedle geometry. These findings provide important insights for optimizing polymeric microneedle designs to enhance strength and skin penetration reliability.

Japan recently experienced a record surge in streptococcal toxic shock syndrome. Our environmental surveillance study reveals that Streptococcus pyogenes persists seasonally, peaking in autumn and winter in rural Japan. The dominant emm1 M1UK sublineage and csrS mutations heighten virulence, highlighting the urgent need for targeted surveillance and interventions.

The COVID-19 pandemic has underscored the pivotal role of vaccines in mitigating the devastating impact of the virus. In Thailand, the vaccination campaign against SARS-CoV-2 began on 28 February 2021, initially prioritizing healthcare professionals before expanding into a nationwide effort on 7 June 2021. This study employs a mathematical model of COVID-19 transmission with vaccination to analyze the impact of Thailand’s COVID-19 vaccination program from 1 March 2021 to 31 December 2022. We specifically assess the potential loss of lives and occurrence of severe infections across various age groups in a hypothetical scenario where vaccines were not administered. By fitting our model with officially reported COVID-19 death data, our analysis reveals that vaccination efforts prevented a total of 300,234 deaths (95% confidence interval: 295,938–304,349) and averted 1.60 million severe COVID-19 infections (95% confidence interval: 1.54–1.65 million). Notably, the elderly population over 80 years old benefited the most from vaccination, with an estimated 84,518 lives saved, constituting 4.28% of this age group. Furthermore, individuals aged between 70 and 74 years experienced the highest reduction in severe infections, with vaccination potentially preventing 8.35% of this age bracket from developing severe COVID-19.

Influenza, an acute respiratory illness, remains a significant public health challenge, contributing substantially to morbidity and mortality worldwide. Its seasonal prevalence exhibits diversity across regions with distinct climates. This study aimed to explore the seasonal patterns of influenza and their correlation with meteorological and air pollution factors across six regions of Thailand. We conducted an analysis of monthly average temperature, relative humidity, precipitation, PM10, NO2, O3 concentrations, and influenza incidence data from 2009 to 2019 using wavelet analysis. Our findings reveal inconsistent biannual influenza prevalence patterns throughout the study period. The biannual pattern emerged during 2010-2012 across all regions but disappeared during 2013-2016. However, post-2016, the biannual cycles resurfaced, with peaks occurring during the rainy and winter seasons in most regions, except for the southern region. Wavelet coherence reveals that relative humidity can be the main influencing factor for influenza incidence over a one-year period in the northern, northeastern, central, Bangkok-metropolitan, and eastern regions, not in the southern region during 2010-2012 and 2016-2018. Similarly, precipitation can drive the influenza incidence at the same period for the northeastern, central, Bangkok-metropolitan, and eastern regions. PM10 concentration can influence influenza incidence over a half-year period in the northeastern, central, Bangkok-metropolitan, and eastern regions of Thailand during certain years. These results enhance our understanding of the temporal dynamics of influenza seasonality influenced by weather conditions and air pollution over the past 11 years. Such knowledge is invaluable for resource allocation in clinical settings and informing public health strategies, particularly in navigating Thailand's climatic complexities.

Mass vaccination has proven to be an effective control measure for mitigating the transmission of infectious diseases. Throughout history, various vaccination strategies have been employed to control infections and terminate outbreaks. In this study, we utilized the transmission of COVID-19 as a case study and constructed a stochastic age-structured compartmental model to investigate the effectiveness of different vaccination strategies. Our analysis focused on estimating the outbreak extinction probability under different vaccination scenarios in both homogeneous and heterogeneous populations. Notably, we found that population heterogeneity can enhance the likelihood of outbreak extinction at varying levels of vaccine coverage. Prioritizing vaccinations for individuals with higher infection risk was found to maximize outbreak extinction probability and reduce overall infections, while allocating vaccines to those with higher mortality risk has been proven more effective in reducing deaths. Moreover, our study highlighted the significance of booster doses as the vaccine effectiveness wanes over time, showing that they can significantly enhance the extinction probability and mitigate disease transmission.

The SARS-CoV-2 variant JN.1 swiftly became the global dominant strain1Yang S Yu Y Xu Y et al.Fast evolution of SARS-CoV-2 BA.2.86 to JN.1 under heavy immune pressure.Lancet Infect Dis. 2024; 24: e70-e72Summary Full Text Full Text PDF PubMed Scopus (18) Google Scholar, 2Wannigama DL Amarasiri M Phattharapornjaroen P et al.Tracing the new SARS-CoV-2 variant BA.2.86 in the community through wastewater surveillance in Bangkok, Thailand.Lancet Infect Dis. 2023; 23: e464-e466Summary Full Text Full Text PDF PubMed Scopus (11) Google Scholar due to a spike protein Leu455Ser substitution, boosting transmissibility and immune-escape capabilities, surpassing its predecessor BA.2.86 and other variants.1Yang S Yu Y Xu Y et al.Fast evolution of SARS-CoV-2 BA.2.86 to JN.1 under heavy immune pressure.Lancet Infect Dis. 2024; 24: e70-e72Summary Full Text Full Text PDF PubMed Scopus (18) Google Scholar, 3Qu P Xu K Faraone JN et al.Immune evasion, infectivity, and fusogenicity of SARS-CoV-2 BA.2.86 and FLip variants.Cell. 2024; 187: 585-595Summary Full Text Full Text PDF PubMed Scopus (7) Google Scholar, 4Kaku Y Okumura K Padilla-Blanco M et al.Virological characteristics of the SARS-CoV-2 JN.1 variant.Lancet Infect Dis. 2024; 24: e82Summary Full Text Full Text PDF PubMed Scopus (13) Google Scholar These alterations have resulted in a surge of COVID-19 cases, reflected in wastewater-surveillance data surpassing rates, observed during the initial omicron wave. However, concerns persist that JN.1 might have an increased capacity to replicate in the gut, potentially leading to infected individuals shedding a higher number of viral copies than previously seen. As there is currently a lack of available data for fecal viral shedding, we are presenting the initial longitudinal and quantitative faecal shedding data for SARS-CoV-2 RNA in individuals infected with XBB.1.5, EG.5.1, HV.1, JD.1.1, BA.2.86, and JN.1. 856 faecal samples were obtained from 113 non-hospitalised individuals with confirmed PCR positivity for SARS-CoV-2 RNA. Variants were identified through Sanger sequencing. Detailed protocols for processing and extracting SARS-CoV-2 RNA from stool samples are provided in the appendix (pp 6–9, 15–19). A summary of the cohort demographics is shown in the appendix (p 10). The cohort contained all individuals (n=113) fully vaccinated for SARS-CoV-2, with eight (7%) having received BA4/5 bivalent booster. The median number of samples collected per individual was eight, with a range of 4–12 samples per individual. A significant difference (p<0·0001) in faecal SARS-CoV-2 RNA concentration was observed among the variants XBB.1.5, EG.5.1, HV.1, JD.1.1, BA.2.86, and JN.1 on days 0, 3, 5, 7, 9, and 15 (figure). Specifically, BA.2.86 and JN.1 exhibited significant differences (p=0·0010) compared with XBB.1.5, EG.5.1, HV.1, and JD.1.1 across symptoms onset days 0, 3, 5, 7, 9, and 15 (figure; appendix p 11). Additionally, significant differences (p = 0·0010) were also noted between EG.5.1 and JD.1.1 or XBB.1.5. Furthermore, a similar significant difference (p=0·0010) between BA.2.86 and JN.1 was consistently observed on symptoms onset days 0, 3, 5, 7, 9, and 15. In nasopharyngeal samples, significant differences in SARS-CoV-2 RNA concentration were observed between JN.1 and BA.2.86 (p=0·0046), and HV.1 (p=0·0014) on day 0 post-PCR positivity. Moreover, on the same day, significant differences were noted between HV.1 and JD.1.1 (p=0·021) and XBB.1.5 (p=0·0056). Additionally, BA.2.86 and XBB.1.5 (p=0·039) displayed significant differences in SARS-CoV-2 RNA concentration on day 0. 37 (33%) individuals did not have symptoms of COVID-19. There were no significant differences in faecal shedding values between symptomatic and asymptomatic individuals on days 0 (p=0·072), 3 (p=0·97), 5 (p=0·061), 9 (p=0·5739), 11 (p=0·567), and 21 (p=0·65) relative to days since PCR positivity for SARS-CoV-2 RNA (figure). However, there was a significant difference in faecal shedding values between day 7 (p=0·0013) and day 15 (p=0·013). Body aches, cough, loss of appetite, chills, diarrhoea, sore throat, and nausea were very common among the JN.1-infected individuals compared with other variants (figure). There was no statistically significant difference in the prevalence of these symptoms among variants (appendix p 14). Symptoms were resolved within 4–5 days in 89% of individuals after symptom onset. 44 (39%) individuals exhibited presymptom onset faecal shedding of SARS-CoV-2 RNA, with stool samples collected 3 days before the onset of symptoms (figure). Taken together, we provide evidence confirming that both BA.2.86 and JN.1 show higher rates of viral shedding compared with XBB.1.5, EG.5.1, HV.1, and JD.1.1, including in presymptomatic and vaccinated individuals, which might partly explain the increase in wastewater levels. Although JN.1 is known for greater transmissibility and immune evasion,1Yang S Yu Y Xu Y et al.Fast evolution of SARS-CoV-2 BA.2.86 to JN.1 under heavy immune pressure.Lancet Infect Dis. 2024; 24: e70-e72Summary Full Text Full Text PDF PubMed Scopus (18) Google Scholar, 4Kaku Y Okumura K Padilla-Blanco M et al.Virological characteristics of the SARS-CoV-2 JN.1 variant.Lancet Infect Dis. 2024; 24: e82Summary Full Text Full Text PDF PubMed Scopus (13) Google Scholar none of our cohort had severe digestive illnesses from JN.1, although it might contribute to more efficient gut tissue infection. This assertion requires confirmation through comprehensive studies. Our findings provide crucial first insights into viral faecal shedding among individuals infected with BA.2.86 and JN.1 variants. This information could be valuable for the clinical and epidemiological management of SARS-CoV-2 as well as those practising wastewater-based epidemiology. For more on COVID-19 variant rates see https://gisaid.org/hcov19-variants For more on COVID-19 variant rates see https://gisaid.org/hcov19-variants MA and CH contributed equally as joint first authors. DLW, CH, and PH: conception, funding acquisition, investigation, data curation, formal analysis, supervision, and writing original draft of the manuscript. MA: conception, investigation, data curation, formal analysis, supervision, and writing original draft of the manuscript. CM: data curation, formal analysis, and supervision. SC and SA: formal analysis, critical review, and editing of the manuscript. PP, KM, LC, SF, ATH, PO, AS, NKDR, DS, TF, KSe, AL, TK, PGH, AN, AKi, RS, ST, AKh, and KSh: supervision, critical review, and editing of the manuscript. TC: critical review and editing of the manuscript. SA and HI: conception, funding acquisition, investigation, supervision, critical review, and editing of the manuscript. DLW was supported by Balvi Philanthropic Fund, Chulalongkorn University (Second Century Fund Postdoctoral Fellowship), University of Western Australia (Overseas Research Experience Fellowship), and Yamagata Prefectural Central Hospital (Clinical Residency Fellowship). CM was supported by the Centre of Excellence in Mathematics, Ministry of Higher Education, Science, Research and Innovation, Centre of Excellence on Medical Biotechnology, and Thailand Centre of Excellence in Physics. ATH is a Herchel Smith Postdoctoral Research Fellow. AKi is a Rothwell Family Fellow. The funders had no role in study design, collection, analysis, or interpretation of data, writing of the report, or in the decision to submit the article for publication. All other authors declare no competing interests. This study was conducted in accordance with the principles of the Declaration of Helsinki, Good Clinical Practice guidelines, and other applicable laws and regulations, including Strengthening the Reporting of Observational studies in Epidemiology guidelines. The study is part of the COVID-19 surveillance study and was reviewed and approved by the institutional review board at Yamagata Prefectural Central Hospital. All volunteers or their legally acceptable representatives provided written informed consent. We thank all the volunteers who kindly supported the sample collection and all the volunteer participants. We thank especially the LGBTQIA+ community in Yamagata for helping with the sample collection. We embrace inclusive, diverse, and equitable conduct of research. Our team comprises of individuals who self-identify as under-represented ethnic minorities, gender minorities, members of the LGBTQIA+ community, and individuals living with disabilities. We actively promote gender balance in our reference list while maintaining scientific relevance. Download .pdf (.6 MB) Help with pdf files Supplementary appendix

Discover the shifting landscape of SARS-CoV-2 variants from October to December 2023, with JN.1 dominating South and Southeast Asia wastewater samples, increasing from <10% to >90%. Experience the dynamic evolution of viral strains in this period.

Rabies is a neglected disease primarily related to dog-mediated transmission to humans. Accurate dog demographic and dynamic data are essential for effectively planning and evaluating population management strategies when designing interventions to prevent rabies. However, in Thailand, longitudinal survey data regarding dog population size are scarce. A school-based participatory research (SBPR) approach was conducted to survey owned dogs for one year in four high-risk provinces (Chiang Rai, Surin, Chonburi, and Songkhla) of Thailand, aiming to understand dog population dynamics and raise awareness about rabies. ‘Pupify’ mobile application was developed to collect data on dog population and observe the long-term population dynamics in this study. At the end of the data collection period, telephone interviews were conducted to gain insight into contextual perceptions and awareness regarding both animal and human rabies, as well as the social responsibility of dog owners in disease prevention and control. Among 303 high school students who registered in our study, 218 students reported at least one update of their dog information throughout the one-year period. Of 322 owned dogs from our survey, the updates of dog status over one year showed approximately 7.5 newborns per 100-dog-year, while deaths and missing dogs were 6.2 and 2.7 per 100-dog-year, respectively. The male to female ratio was approximately 1.8:1. Twenty-three students (10%) voluntarily participated and were interviewed in the qualitative study. The levels of rabies awareness and precautions among high-school students were relatively low. The high dropout rate of the survey was due to discontinuity in communication between the researcher and the students over the year. In conclusion, this study focused on using the SBPR approach via mobile application to collect data informing dog population dynamics and raising awareness regarding rabies in Thailand Other engaging platforms (e.g. Facebook, Instagram, Twitter, and other popular applications) is necessary to enhance communication and engagement, thereby sustaining and maintaining data collection. Further health education on rabies vaccination and animal-care practices via social media platforms would be highly beneficial. For sustainable disease control, engaging communities to raise awareness of rabies and increase dog owners’ understanding of their responsibilities should be encouraged.

The COVID-19 pandemic has highlighted the crucial role of testing in mitigating disease transmission. This study comprehensively evaluates the effectiveness and cost-efficiency of various testing strategies, including daily screening, symptom-based testing, and contact-based testing, using assays such as RT-PCR, RT-LAMP, and antigen tests. Employing stochastic modeling on a contact network, we assessed the impact of these strategies on outbreak control, using COVID-19 as a case study. Our findings demonstrate that daily screening, particularly with RT-PCR and RT-LAMP, significantly reduces transmission risks but incurs higher costs. In contrast, symptom-based testing offers a more cost-effective alternative, albeit with lower efficacy in mitigating outbreaks. Notably, testing turnaround time emerges as a more critical factor than assay sensitivity in containing outbreaks. Moreover, combining symptom-based testing with contact tracing further reduces outbreak probability and scale. To provide a comprehensive analysis, we also explored the application of these strategies in scenarios where a portion of the population has acquired immunity. Our results suggest that testing all symptomatic individuals is the most effective and cost-efficient approach in the later stages of an epidemic. These findings provide valuable insights for optimizing testing strategies to tackle current and future infectious disease outbreaks effectively and efficiently. By adapting strategies based on the stage of the epidemic, population immunity, and available resources, public health authorities can design targeted interventions to protect communities while managing limited resources. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by the Center of Excellence on Medical Biotechnology (CEMB), The Science, Research and Innovation Promotion and Utilization Division, The Office of the Permanent Secretary Ministry of Higher Education, Science and Innovation, Thailand. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript.