Pub Date : 2024-11-12DOI: 10.1016/j.epidem.2024.100801
Karol Niedzielewski , Rafał P. Bartczuk , Natalia Bielczyk , Dominik Bogucki , Filip Dreger , Grzegorz Dudziuk , Łukasz Górski , Magdalena Gruziel-Słomka , Jędrzej Haman , Artur Kaczorek , Jan Kisielewski , Bartosz Krupa , Antoni Moszyński , Jędrzej M. Nowosielski , Maciej Radwan , Marcin Semeniuk , Urszula Tymoszuk , Jakub Zieliński , Franciszek Rakowski
We employ pDyn (derived from “pandemics dynamics”), an agent-based epidemiological model, to forecast the fourth wave of the SARS-CoV-2 epidemic, primarily driven by the Delta variant, in Polish society. The model captures spatiotemporal dynamics of the epidemic spread, predicting disease-related states based on pathogen properties and behavioral factors. We assess pDyn’s validity, encompassing pathogen variant succession, immunization level, and the proportion of vaccinated among confirmed cases. We evaluate its predictive capacity for pandemic dynamics, including wave peak timing, magnitude, and duration for confirmed cases, hospitalizations, ICU admissions, and deaths, nationally and regionally in Poland. Validation involves comparing pDyn’s estimates with real-world data (excluding data used for calibration) to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time. To assess the accuracy of pDyn’s predictions, we compared simulation results with real-world data acquired after the simulation date. The findings affirm pDyn’s accuracy in forecasting and enhancing our understanding of epidemic mechanisms.
{"title":"Forecasting SARS-CoV-2 epidemic dynamic in Poland with the pDyn agent-based model","authors":"Karol Niedzielewski , Rafał P. Bartczuk , Natalia Bielczyk , Dominik Bogucki , Filip Dreger , Grzegorz Dudziuk , Łukasz Górski , Magdalena Gruziel-Słomka , Jędrzej Haman , Artur Kaczorek , Jan Kisielewski , Bartosz Krupa , Antoni Moszyński , Jędrzej M. Nowosielski , Maciej Radwan , Marcin Semeniuk , Urszula Tymoszuk , Jakub Zieliński , Franciszek Rakowski","doi":"10.1016/j.epidem.2024.100801","DOIUrl":"10.1016/j.epidem.2024.100801","url":null,"abstract":"<div><div>We employ pDyn (derived from “pandemics dynamics”), an agent-based epidemiological model, to forecast the fourth wave of the SARS-CoV-2 epidemic, primarily driven by the Delta variant, in Polish society. The model captures spatiotemporal dynamics of the epidemic spread, predicting disease-related states based on pathogen properties and behavioral factors. We assess pDyn’s validity, encompassing pathogen variant succession, immunization level, and the proportion of vaccinated among confirmed cases. We evaluate its predictive capacity for pandemic dynamics, including wave peak timing, magnitude, and duration for confirmed cases, hospitalizations, ICU admissions, and deaths, nationally and regionally in Poland. Validation involves comparing pDyn’s estimates with real-world data (excluding data used for calibration) to evaluate whether pDyn accurately reproduced the epidemic dynamics up to the simulation time. To assess the accuracy of pDyn’s predictions, we compared simulation results with real-world data acquired after the simulation date. The findings affirm pDyn’s accuracy in forecasting and enhancing our understanding of epidemic mechanisms.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100801"},"PeriodicalIF":3.0,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-10DOI: 10.1016/j.epidem.2024.100804
Maria L. Daza-Torres , J. Cricelio Montesinos-López , César Herrera , Yury E. García , Colleen C. Naughton , Heather N. Bischel , Miriam Nuño
In 2022, the US Centers for Disease Control and Prevention commissioned the National Academies of Sciences, Engineering, and Medicine to assess the role of community-level wastewater-based epidemiology (WBE) beyond COVID-19. WBE is recognized as a promising mechanism for promptly identifying infectious diseases, including COVID-19 and other novel pathogens. An important conclusion from this initiative is the critical importance of maintaining equity and expanding access to fully realize the benefits of wastewater surveillance for marginalized communities. To address this need, we propose an optimization framework that strategically allocates wastewater monitoring resources at the wastewater treatment plant (WWTP) level, ensuring more effective and equitable distribution of surveillance efforts to serve underserved populations.
The purpose of the framework is to obtain a balanced spatial distribution, inclusive population coverage, and efficient representation of disadvantaged groups in the allocation of resources for WBE. Furthermore, the framework concentrates on areas with high population density and gives priority to vulnerable regions, as well as identifying signals that display significant variations from other monitored sources. The optimization objective is to maximize a weighted combination of these critical factors. This problem is formulated as an integer optimization problem and solved using simulated annealing. We evaluate various scenarios, considering different weighting factors, to optimize the allocation of WWTPs with monitoring systems. This optimization framework provides an opportunity to enhance WBE by providing customized monitoring strategies created to address specific priorities and situations, thus enhancing the decision-making processes in public health responses.
{"title":"Optimizing spatial distribution of wastewater-based epidemiology to advance health equity","authors":"Maria L. Daza-Torres , J. Cricelio Montesinos-López , César Herrera , Yury E. García , Colleen C. Naughton , Heather N. Bischel , Miriam Nuño","doi":"10.1016/j.epidem.2024.100804","DOIUrl":"10.1016/j.epidem.2024.100804","url":null,"abstract":"<div><div>In 2022, the US Centers for Disease Control and Prevention commissioned the National Academies of Sciences, Engineering, and Medicine to assess the role of community-level wastewater-based epidemiology (WBE) beyond COVID-19. WBE is recognized as a promising mechanism for promptly identifying infectious diseases, including COVID-19 and other novel pathogens. An important conclusion from this initiative is the critical importance of maintaining equity and expanding access to fully realize the benefits of wastewater surveillance for marginalized communities. To address this need, we propose an optimization framework that strategically allocates wastewater monitoring resources at the wastewater treatment plant (WWTP) level, ensuring more effective and equitable distribution of surveillance efforts to serve underserved populations.</div><div>The purpose of the framework is to obtain a balanced spatial distribution, inclusive population coverage, and efficient representation of disadvantaged groups in the allocation of resources for WBE. Furthermore, the framework concentrates on areas with high population density and gives priority to vulnerable regions, as well as identifying signals that display significant variations from other monitored sources. The optimization objective is to maximize a weighted combination of these critical factors. This problem is formulated as an integer optimization problem and solved using simulated annealing. We evaluate various scenarios, considering different weighting factors, to optimize the allocation of WWTPs with monitoring systems. This optimization framework provides an opportunity to enhance WBE by providing customized monitoring strategies created to address specific priorities and situations, thus enhancing the decision-making processes in public health responses.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100804"},"PeriodicalIF":3.0,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-09DOI: 10.1016/j.epidem.2024.100802
Rachael Pung, Adam J Kucharski
Infectious disease models provide a systematic way to estimate crucial features of epidemic dynamics and explore different transmission and control scenarios. Given the importance of model-based analysis in managing public health crises, there has been an increase in post-pandemic creation of both academia-driven modelling centres, hubs and consortiums and government-driven public health agencies with in-house modelling units or teams. However, in the past, the delineation of roles and responsibilities between government- and academia-led modelling groups has often been unclear. Who should perform which tasks and when? This ambiguity can increase the risk of duplicated work or unaddressed gaps in analysis. It also raises questions about the sustainability of modelling capacity for addressing routine operational analytical needs while also developing new approaches that can be tailored for emergencies. In the sections below, we discuss factors that could inform decisions about where to locate infectious disease modelling activity. Rather than giving a fixed set of rules, we outlined key considerations and trade-offs that could be taken into account to enable academic and government modelling activities to complement each other effectively, which can in turn be refined as new public health crises emerge in future.
{"title":"Building in-house capabilities in health agencies and outsourcing to academia or industry: Considerations for effective infectious disease modelling.","authors":"Rachael Pung, Adam J Kucharski","doi":"10.1016/j.epidem.2024.100802","DOIUrl":"https://doi.org/10.1016/j.epidem.2024.100802","url":null,"abstract":"<p><p>Infectious disease models provide a systematic way to estimate crucial features of epidemic dynamics and explore different transmission and control scenarios. Given the importance of model-based analysis in managing public health crises, there has been an increase in post-pandemic creation of both academia-driven modelling centres, hubs and consortiums and government-driven public health agencies with in-house modelling units or teams. However, in the past, the delineation of roles and responsibilities between government- and academia-led modelling groups has often been unclear. Who should perform which tasks and when? This ambiguity can increase the risk of duplicated work or unaddressed gaps in analysis. It also raises questions about the sustainability of modelling capacity for addressing routine operational analytical needs while also developing new approaches that can be tailored for emergencies. In the sections below, we discuss factors that could inform decisions about where to locate infectious disease modelling activity. Rather than giving a fixed set of rules, we outlined key considerations and trade-offs that could be taken into account to enable academic and government modelling activities to complement each other effectively, which can in turn be refined as new public health crises emerge in future.</p>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":" ","pages":"100802"},"PeriodicalIF":3.0,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142683156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-06DOI: 10.1016/j.epidem.2024.100798
Margaret J. de Villiers , Edward de Villiers , Shevanthi Nayagam , Timothy B. Hallett
Population-level vaccination effects of the hepatitis B vaccine were investigated in four low- and middle-income countries with different levels of vertical and horizontal transmission. Indirect vaccination effects constitute a large proportion of overall vaccination effects of the vaccination programmes in all four countries (over 70% by 2030 in all four countries). However, countries with higher levels of vertical transmission benefit less from indirect vaccination effects from the infant hepatitis B vaccine series during the first decades of the vaccination programme, making the birth dose vaccine more important in these countries. Vaccination, even at levels that do not fully control transmission, has a great effect on the development of disease as it also increases the average age of infection, thereby causing a decrease in the number of chronic infections relative to the number of acute infections.
{"title":"Direct and indirect effects of hepatitis B vaccination in four low- and middle-income countries","authors":"Margaret J. de Villiers , Edward de Villiers , Shevanthi Nayagam , Timothy B. Hallett","doi":"10.1016/j.epidem.2024.100798","DOIUrl":"10.1016/j.epidem.2024.100798","url":null,"abstract":"<div><div>Population-level vaccination effects of the hepatitis B vaccine were investigated in four low- and middle-income countries with different levels of vertical and horizontal transmission. Indirect vaccination effects constitute a large proportion of overall vaccination effects of the vaccination programmes in all four countries (over 70% by 2030 in all four countries). However, countries with higher levels of vertical transmission benefit less from indirect vaccination effects from the infant hepatitis B vaccine series during the first decades of the vaccination programme, making the birth dose vaccine more important in these countries. Vaccination, even at levels that do not fully control transmission, has a great effect on the development of disease as it also increases the average age of infection, thereby causing a decrease in the number of chronic infections relative to the number of acute infections.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100798"},"PeriodicalIF":3.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142631155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-18DOI: 10.1016/j.epidem.2024.100795
Sophie Seidel, Tanja Stadler , Timothy G. Vaughan
Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated.
In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease.
This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.
{"title":"Estimating pathogen spread using structured coalescent and birth–death models: A quantitative comparison","authors":"Sophie Seidel, Tanja Stadler , Timothy G. Vaughan","doi":"10.1016/j.epidem.2024.100795","DOIUrl":"10.1016/j.epidem.2024.100795","url":null,"abstract":"<div><div>Elucidating disease spread between subpopulations is crucial in guiding effective disease control efforts. Genomic epidemiology and phylodynamics have emerged as key principles to estimate such spread from pathogen phylogenies derived from molecular data. Two well-established structured phylodynamic methodologies – based on the coalescent and the birth–death model – are frequently employed to estimate viral spread between populations. Nonetheless, these methodologies operate under distinct assumptions whose impact on the accuracy of migration rate inference is yet to be thoroughly investigated.</div><div>In this manuscript, we present a simulation study, contrasting the inferential outcomes of the structured coalescent model with constant population size and the multitype birth–death model with a constant rate. We explore this comparison across a range of migration rates in endemic diseases and epidemic outbreaks. The results of the epidemic outbreak analysis revealed that the birth–death model exhibits a superior ability to retrieve accurate migration rates compared to the coalescent model, regardless of the actual migration rate. Thus, to estimate accurate migration rates, the population dynamics have to be accounted for. On the other hand, for the endemic disease scenario, our investigation demonstrates that both models produce comparable coverage and accuracy of the migration rates, with the coalescent model generating more precise estimates. Regardless of the specific scenario, both models similarly estimated the source location of the disease.</div><div>This research offers tangible modelling advice for infectious disease analysts, suggesting the use of either model for endemic diseases. For epidemic outbreaks, or scenarios with varying population size, structured phylodynamic models relying on the Kingman coalescent with constant population size should be avoided as they can lead to inaccurate estimates of the migration rate. Instead, coalescent models accounting for varying population size or birth–death models should be favoured. Importantly, our study emphasises the value of directly capturing exponential growth dynamics which could be a useful enhancement for structured coalescent models.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100795"},"PeriodicalIF":3.0,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-11DOI: 10.1016/j.epidem.2024.100799
Alexander Tulchinsky , Gary Lin , Alisa Hamilton , Nodar Kipshidze , Eili Klein
The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model’s potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.
{"title":"Quantifying the impact of prevalence-dependent adaptive behavior on COVID-19 transmission: A modeling case study in Maryland","authors":"Alexander Tulchinsky , Gary Lin , Alisa Hamilton , Nodar Kipshidze , Eili Klein","doi":"10.1016/j.epidem.2024.100799","DOIUrl":"10.1016/j.epidem.2024.100799","url":null,"abstract":"<div><div>The COVID-19 pandemic highlighted the need for robust epidemic forecasts, projecting health burden over short- and medium-term time horizons. Many COVID-19 forecasting models incorporate information on infection transmission, disease progression, and the effects of interventions, but few combine information on how individuals change their behavior based on altruism, fear, risk perception, or personal economic circumstances. Moreover, early models of COVID-19 produced under- and over-estimates, failing to consider the complexity of human responses to disease threat and prevention measures. In this study, we modeled adaptive behavior during the first year of the COVID-19 pandemic in Maryland, USA. The adapted compartmental model incorporates time-varying transmissibility informed on data of environmental factors (e.g., absolute humidity) and behavioral factors (aggregate mobility and perceived risk). We show that humidity and mobility alone did little to explain transmissibility after the first 100 days. Including adaptive behavior in the form of perceived risk as a function of hospitalizations more effectively explained inferred transmissibility and improved out-of-sample fit, demonstrating the model’s potential in real-time forecasting. These results demonstrate the importance of incorporating endogenous behavior in models, particularly during a pandemic, to produce more accurate projections, which could lead to more impactful and efficient decision making and resource allocation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100799"},"PeriodicalIF":3.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142445877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-10DOI: 10.1016/j.epidem.2024.100797
Yiu Chung Lau , Sukhyun Ryu , Zhanwei Du , Lin Wang , Peng Wu , Eric H.Y. Lau , Benjamin J. Cowling , Sheikh Taslim Ali
The public health and social measures (PHSMs) for mitigation/control of COVID-19 pandemic influenced the transmission dynamics of many other infectious diseases, including respiratory syncytial virus (RSV) infection, and hand, foot and mouth disease (HFMD) and their disease-burden. This study aimed to infer the transmission dynamics of these respiratory viruses and assess the impact of COVID-19 PHSMs on their community activity. We developed a compartmental framework to infer the transmission dynamics of RSV and HFMD in Hong Kong and South Korea from January 2014 to May 2024. We assessed the impact of PHSMs by comparing the change in virus transmissibility, reproduction number and population susceptibility before, during, and after the COVID-19 pandemic period. A significant reduction in RSV and HFMD activity was observed starting in January 2020, with a resurgence since late 2021. Transmissibility of both diseases decreased by 46 % - 95 % during the lull, while population susceptibility was estimated to increase by maximum of 19 %. On relaxation of the PHSMs, the transmissibility were recovered up to 70 % in Hong Kong and nearly 100 % in South Korea in 2023 with significant epidemics for these viruses. Strict implementation of COVID-19 PHSMs led to low RSV and HFMD activity, but the absence of community infection resulted in reductions in population immunity, and slightly larger epidemics when these diseases re-emerged following the COVID-19 pandemic.
{"title":"Impact of COVID-19 control measures on respiratory syncytial virus and hand-foot-and-mouth disease transmission in Hong Kong and South Korea","authors":"Yiu Chung Lau , Sukhyun Ryu , Zhanwei Du , Lin Wang , Peng Wu , Eric H.Y. Lau , Benjamin J. Cowling , Sheikh Taslim Ali","doi":"10.1016/j.epidem.2024.100797","DOIUrl":"10.1016/j.epidem.2024.100797","url":null,"abstract":"<div><div>The public health and social measures (PHSMs) for mitigation/control of COVID-19 pandemic influenced the transmission dynamics of many other infectious diseases, including respiratory syncytial virus (RSV) infection, and hand, foot and mouth disease (HFMD) and their disease-burden. This study aimed to infer the transmission dynamics of these respiratory viruses and assess the impact of COVID-19 PHSMs on their community activity. We developed a compartmental framework to infer the transmission dynamics of RSV and HFMD in Hong Kong and South Korea from January 2014 to May 2024. We assessed the impact of PHSMs by comparing the change in virus transmissibility, reproduction number and population susceptibility before, during, and after the COVID-19 pandemic period. A significant reduction in RSV and HFMD activity was observed starting in January 2020, with a resurgence since late 2021. Transmissibility of both diseases decreased by 46 % - 95 % during the lull, while population susceptibility was estimated to increase by maximum of 19 %. On relaxation of the PHSMs, the transmissibility were recovered up to 70 % in Hong Kong and nearly 100 % in South Korea in 2023 with significant epidemics for these viruses. Strict implementation of COVID-19 PHSMs led to low RSV and HFMD activity, but the absence of community infection resulted in reductions in population immunity, and slightly larger epidemics when these diseases re-emerged following the COVID-19 pandemic.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100797"},"PeriodicalIF":3.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142478826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-05DOI: 10.1016/j.epidem.2024.100796
Alex Viguerie , Chaitra Gopalappa , Cynthia M. Lyles , Paul G. Farnham
Background
The OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could enhance both HIV awareness and reduce HIV incidence. However, currently available self-tests are less sensitive, particularly for recent infection, when compared to gold-standard laboratory tests. It is important to understand the impact if some portion of standard testing is replaced by self-tests. We used a compartmental model to evaluate the effects of self-testing in diverse scenarios among gay, bisexual and other men who have sex with men (MSM) in the United States for the period 2020–2030, and to understand which scenarios maximize the advantages of self-testing.
Methods
We introduced a novel 4-compartment model for HIV self-testing. We employed the model under different screening rates, self-test proportions, and delays to diagnosis for those identified through self-tests to determine the potential effects of self-testing on HIV incidence and awareness of status when applied to the US MSM population. We studied scenarios in which self-tests supplement laboratory-based tests, with no replacement, and scenarios in which some replacement occurs. We also examined how future improvements in self-test sensitivity may affect our results.
Results
When HIV self-tests are supplemental rather than substitutes for laboratory-based testing, self-testing can decrease HIV incidence among MSM in the US by up to 10 % and increase awareness of status among MSM from 85 % to 91 % over a 10-year period, provided linkage to care and formal diagnosis occur promptly following a positive self-test (90 days or less). As self-tests replace a higher percentage laboratory-based testing algorithms, increases in overall testing rates were necessary to ensure reductions in HIV incidence. However, such needed increases were relatively small (under 10 % for prompt engagement in care and moderate levels of replacement). Improvements in self-test sensitivity and/or decreases in the detection period may further reduce any necessary increases in overall testing by up to 40 %.
Conclusions
If properly utilized, self-testing can provide significant long-term reductions to HIV incidence and improve awareness of HIV status. Ensuring that self-testing increases overall testing and that formal diagnosis and engagement in care occur promptly following a positive self-test are necessary to maximize the benefits of self-testing. Future improvements in self-test sensitivity and reductions in the detection period would further reduce HIV incidence and the potential risks associated with replacing laboratory tests with self-tests.
背景：OraQuick 居家 HIV 自我检测是一种快速、廉价、方便的方法，用户可以通过它来评估自己的 HIV 感染状况。如果能将其与现有的检测方法周到地结合起来，并辅以高效的正规诊断途径，那么自我检测既能提高人们对艾滋病的认识，又能降低艾滋病的发病率。然而，与黄金标准的实验室检测相比，目前可用的自我检测灵敏度较低，尤其是对近期感染的检测。了解由自我检测取代部分标准检测的影响非常重要。我们使用了一个分室模型来评估 2020-2030 年期间在美国男同性恋、双性恋和其他男男性行为者（MSM）中不同情况下自我检测的影响，并了解哪种情况能最大限度地发挥自我检测的优势：方法：我们为 HIV 自我检测引入了一个新颖的四室模型。我们在不同的筛查率、自我检测比例以及通过自我检测发现者的诊断延迟条件下使用了该模型，以确定自我检测在应用于美国男男性行为人群时对 HIV 感染率和感染状况认知度的潜在影响。我们研究了自我检测作为实验室检测的补充（不存在替代）和存在一定替代的两种情况。我们还研究了未来自我检测灵敏度的提高会如何影响我们的结果：结果：如果艾滋病毒自我检测是实验室检测的补充而非替代，那么自我检测可在 10 年内将美国男男性行为者中的艾滋病毒发病率降低 10%，并将男男性行为者对自身状况的认知度从 85% 提高到 91%，前提是在自我检测呈阳性后（90 天或更短）及时联系医疗机构并进行正式诊断。由于自我检测取代了较高比例的实验室检测算法，因此有必要提高总体检测率，以确保降低艾滋病毒发病率。然而，这种所需的提高幅度相对较小（对于及时参与护理和中等程度的替代来说，低于 10%）。自我检测灵敏度的提高和/或检测期的缩短可能会进一步减少总体检测率的必要增长，增幅可达 40%：如果使用得当，自我检测可显著降低艾滋病的长期发病率，并提高人们对艾滋病状况的认识。要使自我检测的益处最大化，就必须确保自我检测能提高总体检测率，并在自我检测呈阳性后及时进行正式诊断和参与护理。未来自我检测灵敏度的提高和检测周期的缩短将进一步降低艾滋病毒的发病率，并降低用自我检测取代实验室检测的潜在风险。
{"title":"The effects of HIV self-testing on HIV incidence and awareness of status among men who have sex with men in the United States: Insights from a novel compartmental model","authors":"Alex Viguerie , Chaitra Gopalappa , Cynthia M. Lyles , Paul G. Farnham","doi":"10.1016/j.epidem.2024.100796","DOIUrl":"10.1016/j.epidem.2024.100796","url":null,"abstract":"<div><h3>Background</h3><div>The OraQuick In-Home HIV self-test represents a fast, inexpensive, and convenient method for users to assess their HIV status. If integrated thoughtfully into existing testing practices, accompanied by efficient pathways to formal diagnosis, self-testing could enhance both HIV awareness and reduce HIV incidence. However, currently available self-tests are less sensitive, particularly for recent infection, when compared to gold-standard laboratory tests. It is important to understand the impact if some portion of standard testing is replaced by self-tests. We used a compartmental model to evaluate the effects of self-testing in diverse scenarios among gay, bisexual and other men who have sex with men (MSM) in the United States for the period 2020–2030, and to understand which scenarios maximize the advantages of self-testing.</div></div><div><h3>Methods</h3><div>We introduced a novel 4-compartment model for HIV self-testing. We employed the model under different screening rates, self-test proportions, and delays to diagnosis for those identified through self-tests to determine the potential effects of self-testing on HIV incidence and awareness of status when applied to the US MSM population. We studied scenarios in which self-tests supplement laboratory-based tests, with no replacement, and scenarios in which some replacement occurs. We also examined how future improvements in self-test sensitivity may affect our results.</div></div><div><h3>Results</h3><div>When HIV self-tests are supplemental rather than substitutes for laboratory-based testing, self-testing can decrease HIV incidence among MSM in the US by up to 10 % and increase awareness of status among MSM from 85 % to 91 % over a 10-year period, provided linkage to care and formal diagnosis occur promptly following a positive self-test (90 days or less). As self-tests replace a higher percentage laboratory-based testing algorithms, increases in overall testing rates were necessary to ensure reductions in HIV incidence. However, such needed increases were relatively small (under 10 % for prompt engagement in care and moderate levels of replacement). Improvements in self-test sensitivity and/or decreases in the detection period may further reduce any necessary increases in overall testing by up to 40 %.</div></div><div><h3>Conclusions</h3><div>If properly utilized, self-testing can provide significant long-term reductions to HIV incidence and improve awareness of HIV status. Ensuring that self-testing increases overall testing and that formal diagnosis and engagement in care occur promptly following a positive self-test are necessary to maximize the benefits of self-testing. Future improvements in self-test sensitivity and reductions in the detection period would further reduce HIV incidence and the potential risks associated with replacing laboratory tests with self-tests.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100796"},"PeriodicalIF":3.0,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142382152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-26DOI: 10.1016/j.epidem.2024.100793
Chen Chen , Yunfan Wang , Gursharn Kaur , Aniruddha Adiga , Baltazar Espinoza , Srinivasan Venkatramanan , Andrew Warren , Bryan Lewis , Justin Crow , Rekha Singh , Alexandra Lorentz , Denise Toney , Madhav Marathe
The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.
{"title":"Wastewater-based epidemiology for COVID-19 surveillance and beyond: A survey","authors":"Chen Chen , Yunfan Wang , Gursharn Kaur , Aniruddha Adiga , Baltazar Espinoza , Srinivasan Venkatramanan , Andrew Warren , Bryan Lewis , Justin Crow , Rekha Singh , Alexandra Lorentz , Denise Toney , Madhav Marathe","doi":"10.1016/j.epidem.2024.100793","DOIUrl":"10.1016/j.epidem.2024.100793","url":null,"abstract":"<div><div>The pandemic of COVID-19 has imposed tremendous pressure on public health systems and social economic ecosystems over the past years. To alleviate its social impact, it is important to proactively track the prevalence of COVID-19 within communities. The traditional way to estimate the disease prevalence is to estimate from reported clinical test data or surveys. However, the coverage of clinical tests is often limited and the tests can be labor-intensive, requires reliable and timely results, and consistent diagnostic and reporting criteria. Recent studies revealed that patients who are diagnosed with COVID-19 often undergo fecal shedding of SARS-CoV-2 virus into wastewater, which makes wastewater-based epidemiology for COVID-19 surveillance a promising approach to complement traditional clinical testing. In this paper, we survey the existing literature regarding wastewater-based epidemiology for COVID-19 surveillance and summarize the current advances in the area. Specifically, we have covered the key aspects of wastewater sampling, sample testing, and presented a comprehensive and organized summary of wastewater data analytical methods. Finally, we provide the open challenges on current wastewater-based COVID-19 surveillance studies, aiming to encourage new ideas to advance the development of effective wastewater-based surveillance systems for general infectious diseases.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100793"},"PeriodicalIF":3.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1016/j.epidem.2024.100794
Hélène Duault , Benoit Durand , Laetitia Canini
In a multi-host system, understanding host-species contribution to transmission is key to appropriately targeting control and preventive measures. Outbreak reconstruction methods aiming to identify who-infected-whom by combining epidemiological and genetic data could contribute to achieving this goal. However, the majority of these methods remain untested on realistic simulated multi-host data. Mycobacterium bovis is a slowly evolving multi-host pathogen and previous studies on outbreaks involving both cattle and wildlife have identified observation biases. Indeed, contrary to cattle, sampling wildlife is difficult. The aim of our study was to evaluate and compare the performances of three existing outbreak reconstruction methods (seqTrack, outbreaker2 and TransPhylo) on M. bovis multi-host data simulated with and without biases. Extending an existing transmission model, we simulated 30 bTB outbreaks involving cattle, badgers and wild boars and defined six sampling schemes mimicking observation biases. We estimated general and specific to multi-host systems epidemiological indicators. We tested four alternative transmission scenarios changing the mutation rate or the composition of the epidemiological system. The reconstruction of who-infected-whom was sensitive to the mutation rate and seqTrack reconstructed prolific super-spreaders. TransPhylo and outbreaker2 poorly estimated the contribution of each host-species and could not reconstruct the presence of a dead-end epidemiological host. However, the host-species of cattle (but not badger) index cases was correctly reconstructed by seqTrack and outbreaker2. These two specific indicators improved when considering an observation bias. We found an overall poor performance for the three methods on simulated biased and unbiased bTB data. This seemed partly attributable to the low evolutionary rate characteristic of M. bovis leading to insufficient genetic information, but also to the complexity of the simulated multi-host system. This study highlights the importance of an integrated approach and the need to develop new outbreak reconstruction methods adapted to complex epidemiological systems and tested on realistic multi-host data.
{"title":"Outbreak reconstruction with a slowly evolving multi-host pathogen: A comparative study of three existing methods on Mycobacterium bovis outbreaks","authors":"Hélène Duault , Benoit Durand , Laetitia Canini","doi":"10.1016/j.epidem.2024.100794","DOIUrl":"10.1016/j.epidem.2024.100794","url":null,"abstract":"<div><div>In a multi-host system, understanding host-species contribution to transmission is key to appropriately targeting control and preventive measures. Outbreak reconstruction methods aiming to identify who-infected-whom by combining epidemiological and genetic data could contribute to achieving this goal. However, the majority of these methods remain untested on realistic simulated multi-host data. <em>Mycobacterium bovis</em> is a slowly evolving multi-host pathogen and previous studies on outbreaks involving both cattle and wildlife have identified observation biases. Indeed, contrary to cattle, sampling wildlife is difficult. The aim of our study was to evaluate and compare the performances of three existing outbreak reconstruction methods (<em>seqTrack</em>, <em>outbreaker2</em> and <em>TransPhylo</em>) on <em>M. bovis</em> multi-host data simulated with and without biases. Extending an existing transmission model, we simulated 30 bTB outbreaks involving cattle, badgers and wild boars and defined six sampling schemes mimicking observation biases. We estimated general and specific to multi-host systems epidemiological indicators. We tested four alternative transmission scenarios changing the mutation rate or the composition of the epidemiological system. The reconstruction of who-infected-whom was sensitive to the mutation rate and <em>seqTrack</em> reconstructed prolific super-spreaders. <em>TransPhylo</em> and <em>outbreaker2</em> poorly estimated the contribution of each host-species and could not reconstruct the presence of a dead-end epidemiological host. However, the host-species of cattle (but not badger) index cases was correctly reconstructed by <em>seqTrack</em> and <em>outbreaker2</em>. These two specific indicators improved when considering an observation bias. We found an overall poor performance for the three methods on simulated biased and unbiased bTB data. This seemed partly attributable to the low evolutionary rate characteristic of <em>M. bovis</em> leading to insufficient genetic information, but also to the complexity of the simulated multi-host system. This study highlights the importance of an integrated approach and the need to develop new outbreak reconstruction methods adapted to complex epidemiological systems and tested on realistic multi-host data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"49 ","pages":"Article 100794"},"PeriodicalIF":3.0,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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