Pub Date : 2025-02-07DOI: 10.1016/j.epidem.2025.100816
Austin G. Meyer , Fred Lu , Leonardo Clemente , Mauricio Santillana
Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-the-art machine learning methods are a promising approach to produce such forecasts, but they require extensive historical data to be properly trained. Unfortunately, data on influenza hospitalizations, for the 50 states in the United States, are only available since the beginning of 2020. In addition, the data are far from perfect as they were under-reported for several months before health systems began consistently submitting their data. To address these issues, we propose a transfer learning approach. We extend the currently available two-season dataset for state-level influenza hospitalizations by an additional ten seasons. Our method leverages influenza-like illness (ILI) data to infer historical estimates of influenza hospitalizations. This data augmentation enables the implementation of advanced machine learning techniques, multi-horizon training, and an ensemble of models to improve hospitalization forecasts. We evaluated the performance of our machine learning approaches by prospectively producing forecasts for future weeks and submitting them in real time to the Centers for Disease Control and Prevention FluSight challenges during two seasons: 2022–2023 and 2023–2024. Our methodology demonstrated good accuracy and reliability, achieving a fourth place finish (among 20 participating teams) in the 2022–23 and a second place finish (among 20 participating teams) in the 2023–24 CDC FluSight challenges. Our findings highlight the utility of data augmentation and knowledge transfer in the application of machine learning models to public health surveillance where only limited historical data is available.
{"title":"A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data","authors":"Austin G. Meyer , Fred Lu , Leonardo Clemente , Mauricio Santillana","doi":"10.1016/j.epidem.2025.100816","DOIUrl":"10.1016/j.epidem.2025.100816","url":null,"abstract":"<div><div>Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-the-art machine learning methods are a promising approach to produce such forecasts, but they require extensive historical data to be properly trained. Unfortunately, data on influenza hospitalizations, for the 50 states in the United States, are only available since the beginning of 2020. In addition, the data are far from perfect as they were under-reported for several months before health systems began consistently submitting their data. To address these issues, we propose a transfer learning approach. We extend the currently available two-season dataset for state-level influenza hospitalizations by an additional ten seasons. Our method leverages influenza-like illness (ILI) data to infer historical estimates of influenza hospitalizations. This data augmentation enables the implementation of advanced machine learning techniques, multi-horizon training, and an ensemble of models to improve hospitalization forecasts. We evaluated the performance of our machine learning approaches by prospectively producing forecasts for future weeks and submitting them in real time to the Centers for Disease Control and Prevention FluSight challenges during two seasons: 2022–2023 and 2023–2024. Our methodology demonstrated good accuracy and reliability, achieving a fourth place finish (among 20 participating teams) in the 2022–23 and a second place finish (among 20 participating teams) in the 2023–24 CDC FluSight challenges. Our findings highlight the utility of data augmentation and knowledge transfer in the application of machine learning models to public health surveillance where only limited historical data is available.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100816"},"PeriodicalIF":3.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143464330","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 : 2025-01-26DOI: 10.1016/j.epidem.2025.100818
Matteo Perini , Teresa K. Yamana , Marta Galanti , Jiyeon Suh , Roselyn Kaondera-Shava , Jeffrey Shaman
Background
Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions.
Methods
We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis.
Results
This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.
Conclusions
The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.
{"title":"Modelling COVID-19 in the North American region with a metapopulation network and Kalman filter","authors":"Matteo Perini , Teresa K. Yamana , Marta Galanti , Jiyeon Suh , Roselyn Kaondera-Shava , Jeffrey Shaman","doi":"10.1016/j.epidem.2025.100818","DOIUrl":"10.1016/j.epidem.2025.100818","url":null,"abstract":"<div><h3>Background</h3><div>Understanding the dynamics of infectious disease spread and predicting clinical outcomes are critical for managing large-scale epidemics and pandemics, such as COVID-19. Effective modeling of disease transmission in interconnected populations helps inform public health responses and interventions across regions.</div></div><div><h3>Methods</h3><div>We developed a novel metapopulation model for simulating respiratory virus transmission in the North America region, specifically for the 96 states, provinces, and territories of Canada, Mexico, and the United States. The model is informed by COVID-19 case data, which are assimilated using the Ensemble Adjustment Kalman filter (EAKF), a Bayesian inference algorithm. Additionally, commuting and mobility data are used to build and adjust the network and movement across locations on a daily basis.</div></div><div><h3>Results</h3><div>This model-inference system provides estimates of transmission dynamics, infection rates, and ascertainment rates for each of the 96 locations from January 2020 to March 2021. The results highlight differences in disease dynamics and ascertainment among the three countries.</div></div><div><h3>Conclusions</h3><div>The metapopulation structure enables rapid simulation at a large scale, and the data assimilation method makes the system responsive to changes in system dynamics. This model can serve as a versatile platform for modeling other infectious diseases across the North American region.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100818"},"PeriodicalIF":3.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143076069","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 : 2025-01-25DOI: 10.1016/j.epidem.2025.100817
Jaime Cascante Vega , Rami Yaari , Tal Robin , Lingsheng Wen , Jason Zucker , Anne-Catrin Uhlemann , Sen Pei , Jeffrey Shaman
Pathogenic bacteria are a major threat to patient health in hospitals. Here we leverage electronic health records from a major New York City hospital system collected during 2020–2021 to support simulation inference of nosocomial transmission and pathogenic bacteria detection using an agent-based model (ABM). The ABM uses these data to inform simulation of importation from the community, nosocomial transmission, and patient spontaneous decolonization of bacteria. We additionally use patient clinical culture results to inform an observational model of detection of the pathogenic bacteria. The model is coupled with a Bayesian inference algorithm, an iterated ensemble adjustment Kalman filter, to estimate the likelihood of detection upon testing and nosocomial transmission rates. We evaluate parameter identifiability for this model-inference system and find that the system is able to estimate modelled nosocomial transmission and effective sensitivity upon clinical culture testing. We apply the framework to estimate both quantities for seven prevalent bacterial pathogens: Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus (both sensitive, MSSA, and resistant, MRSA, phenotypes), Enterococcus faecium and Enterococcus faecalis. We estimate that nosocomial transmission for E. coli is negligible. While bacterial pathogens have different importation rates, nosocomial transmission rates were similar among organisms, except E. coli. We also find that estimated likelihoods of detection are similar for all pathogens. This work highlights how fine-scale patient data can support inference of the epidemiological properties of micro-organisms and how hospital traffic and patient contact determine epidemiological features. Evaluation of the transmission potential for different pathogens could ultimately support the development of hospital control measures, as well as the design of surveillance strategies.
{"title":"Estimating nosocomial transmission of micro-organisms in hospital settings using patient records and culture data","authors":"Jaime Cascante Vega , Rami Yaari , Tal Robin , Lingsheng Wen , Jason Zucker , Anne-Catrin Uhlemann , Sen Pei , Jeffrey Shaman","doi":"10.1016/j.epidem.2025.100817","DOIUrl":"10.1016/j.epidem.2025.100817","url":null,"abstract":"<div><div>Pathogenic bacteria are a major threat to patient health in hospitals. Here we leverage electronic health records from a major New York City hospital system collected during 2020–2021 to support simulation inference of nosocomial transmission and pathogenic bacteria detection using an agent-based model (ABM). The ABM uses these data to inform simulation of importation from the community, nosocomial transmission, and patient spontaneous decolonization of bacteria. We additionally use patient clinical culture results to inform an observational model of detection of the pathogenic bacteria. The model is coupled with a Bayesian inference algorithm, an iterated ensemble adjustment Kalman filter, to estimate the likelihood of detection upon testing and nosocomial transmission rates. We evaluate parameter identifiability for this model-inference system and find that the system is able to estimate modelled nosocomial transmission and effective sensitivity upon clinical culture testing. We apply the framework to estimate both quantities for seven prevalent bacterial pathogens: <em>Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Staphylococcus aureus</em> (both sensitive, MSSA, and resistant, MRSA, phenotypes), <em>Enterococcus faecium</em> and <em>Enterococcus faecalis</em>. We estimate that nosocomial transmission for <em>E. coli</em> is negligible<em>.</em> While bacterial pathogens have different importation rates, nosocomial transmission rates were similar among organisms, except <em>E. coli</em>. We also find that estimated likelihoods of detection are similar for all pathogens. This work highlights how fine-scale patient data can support inference of the epidemiological properties of micro-organisms and how hospital traffic and patient contact determine epidemiological features. Evaluation of the transmission potential for different pathogens could ultimately support the development of hospital control measures, as well as the design of surveillance strategies.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100817"},"PeriodicalIF":3.0,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394841","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 : 2025-01-18DOI: 10.1016/j.epidem.2025.100815
Louis Yat Hin Chan , Sinead E. Morris , Melissa S. Stockwell , Natalie M. Bowman , Edwin Asturias , Suchitra Rao , Karen Lutrick , Katherine D. Ellingson , Huong Q. Nguyen , Yvonne Maldonado , Son H. McLaren , Ellen Sano , Jessica E. Biddle , Sarah E. Smith-Jeffcoat , Matthew Biggerstaff , Melissa A. Rolfes , H. Keipp Talbot , Carlos G. Grijalva , Rebecca K. Borchering , Alexandra M. Mellis
The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially since the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days, completing daily symptom diaries and collecting nasal swabs, which were then tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission occurring before symptomatic onset, the infectious period, and the latent period. We estimated a mean intrinsic generation time of 3.2 (95 % credible interval, CrI: 2.9–3.6) days, with a realized household generation time of 2.8 (95 % CrI: 2.7–3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variations in the incubation period. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.
{"title":"Estimating the generation time for influenza transmission using household data in the United States","authors":"Louis Yat Hin Chan , Sinead E. Morris , Melissa S. Stockwell , Natalie M. Bowman , Edwin Asturias , Suchitra Rao , Karen Lutrick , Katherine D. Ellingson , Huong Q. Nguyen , Yvonne Maldonado , Son H. McLaren , Ellen Sano , Jessica E. Biddle , Sarah E. Smith-Jeffcoat , Matthew Biggerstaff , Melissa A. Rolfes , H. Keipp Talbot , Carlos G. Grijalva , Rebecca K. Borchering , Alexandra M. Mellis","doi":"10.1016/j.epidem.2025.100815","DOIUrl":"10.1016/j.epidem.2025.100815","url":null,"abstract":"<div><div>The generation time, representing the interval between infections in primary and secondary cases, is essential for understanding and predicting the transmission dynamics of seasonal influenza, including the real-time effective reproduction number (Rt). However, comprehensive generation time estimates for seasonal influenza, especially since the 2009 influenza pandemic, are lacking. We estimated the generation time utilizing data from a 7-site case-ascertained household study in the United States over two influenza seasons, 2021/2022 and 2022/2023. More than 200 individuals who tested positive for influenza and their household contacts were enrolled within 7 days of the first illness in the household. All participants were prospectively followed for 10 days, completing daily symptom diaries and collecting nasal swabs, which were then tested for influenza via RT-PCR. We analyzed these data by modifying a previously published Bayesian data augmentation approach that imputes infection times of cases to obtain both intrinsic (assuming no susceptible depletion) and realized (observed within household) generation times. We assessed the robustness of the generation time estimate by varying the incubation period, and generated estimates of the proportion of transmission occurring before symptomatic onset, the infectious period, and the latent period. We estimated a mean intrinsic generation time of 3.2 (95 % credible interval, CrI: 2.9–3.6) days, with a realized household generation time of 2.8 (95 % CrI: 2.7–3.0) days. The generation time exhibited limited sensitivity to incubation period variation. Estimates of the proportion of transmission that occurred before symptom onset, the infectious period, and the latent period were sensitive to variations in the incubation period. Our study contributes to the ongoing efforts to refine estimates of the generation time for influenza. Our estimates, derived from recent data following the COVID-19 pandemic, are consistent with previous pre-pandemic estimates, and will be incorporated into real-time Rt estimation efforts.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100815"},"PeriodicalIF":3.0,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048454","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 : 2025-01-14DOI: 10.1016/j.epidem.2025.100814
Siyu Chen , Jennifer A. Flegg , Katrina A. Lythgoe , Lisa J. White
Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it was particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. We previously developed an early pandemic diagnostic tool that linked minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure to be considerably higher than suggested by seroprevalence surveys. Here, we re-examine and evaluate the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation against the Office for National Statistics (ONS) Coronavirus Infection Survey, relationship among model performance and data abundance and time-varying case detection ratios. We find that our model can recover the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 if two or three serological measurements are given as additional model inputs, while the second wave during winter of 2020 is validated by estimates from the ONS Coronavirus Infection Survey. Moreover, the model estimates that by the end of October in 2020 the UK government’s official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1 % of cumulative exposure, dramatically varying across the two epidemic waves in England in 2020, 4.3 % vs 43.7 %.
{"title":"Reconstructing the first COVID-19 pandemic wave with minimal data in England","authors":"Siyu Chen , Jennifer A. Flegg , Katrina A. Lythgoe , Lisa J. White","doi":"10.1016/j.epidem.2025.100814","DOIUrl":"10.1016/j.epidem.2025.100814","url":null,"abstract":"<div><div>Accurate measurement of exposure to SARS-CoV-2 in the population is crucial for understanding the dynamics of disease transmission and evaluating the impacts of interventions. However, it was particularly challenging to achieve this in the early phase of a pandemic because of the sparsity of epidemiological data. We previously developed an early pandemic diagnostic tool that linked minimum datasets: seroprevalence, mortality and infection testing data to estimate the true exposure in different regions of England and found levels of SARS-CoV-2 population exposure to be considerably higher than suggested by seroprevalence surveys. Here, we re-examine and evaluate the model in the context of reconstructing the first COVID-19 epidemic wave in England from three perspectives: validation against the Office for National Statistics (ONS) Coronavirus Infection Survey, relationship among model performance and data abundance and time-varying case detection ratios. We find that our model can recover the first, unobserved, epidemic wave of COVID-19 in England from March 2020 to June 2020 if two or three serological measurements are given as additional model inputs, while the second wave during winter of 2020 is validated by estimates from the ONS Coronavirus Infection Survey. Moreover, the model estimates that by the end of October in 2020 the UK government’s official COVID-9 online dashboard reported COVID-19 cases only accounted for 9.1 % of cumulative exposure, dramatically varying across the two epidemic waves in England in 2020, 4.3 % vs 43.7 %.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100814"},"PeriodicalIF":3.0,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014802","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 : 2025-01-13DOI: 10.1016/j.epidem.2024.100812
James Petrie , James A. Hay , Oraya Srimokla , Jasmina Panovska-Griffiths , Charles Whittaker , Joanna Masel
Mass testing to identify and isolate infected individuals is a promising approach for reducing harm from the next acute respiratory virus pandemic. It offers the prospect of averting hospitalizations and deaths whilst avoiding the need for indiscriminate social distancing measures. To understand scenarios where mass testing might or might not be a viable intervention, here we modeled how effectiveness depends both on characteristics of the pathogen (, time to peak viral load) and on the testing strategy (limit of detection, testing frequency, test turnaround time, adherence). We base time-dependent test sensitivity and time-dependent infectiousness on an underlying viral load trajectory model. We show that given moderately high public adherence, frequent testing can prevent as many transmissions as more costly interventions such as school or business closures. With very high adherence and fast, frequent, and sensitive testing, we show that most respiratory virus pandemics could be controlled with mass testing alone.
{"title":"Enhanced testing can substantially improve defense against several types of respiratory virus pandemic","authors":"James Petrie , James A. Hay , Oraya Srimokla , Jasmina Panovska-Griffiths , Charles Whittaker , Joanna Masel","doi":"10.1016/j.epidem.2024.100812","DOIUrl":"10.1016/j.epidem.2024.100812","url":null,"abstract":"<div><div>Mass testing to identify and isolate infected individuals is a promising approach for reducing harm from the next acute respiratory virus pandemic. It offers the prospect of averting hospitalizations and deaths whilst avoiding the need for indiscriminate social distancing measures. To understand scenarios where mass testing might or might not be a viable intervention, here we modeled how effectiveness depends both on characteristics of the pathogen (<span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>, time to peak viral load) and on the testing strategy (limit of detection, testing frequency, test turnaround time, adherence). We base time-dependent test sensitivity and time-dependent infectiousness on an underlying viral load trajectory model. We show that given moderately high public adherence, frequent testing can prevent as many transmissions as more costly interventions such as school or business closures. With very high adherence and fast, frequent, and sensitive testing, we show that most respiratory virus pandemics could be controlled with mass testing alone.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100812"},"PeriodicalIF":3.0,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143349103","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 : 2025-01-11DOI: 10.1016/j.epidem.2025.100813
Ella Ziegler , Katarina L. Matthes , Peter W. Middelkamp , Verena J. Schuenemann , Christian L. Althaus , Frank Rühli , Kaspar Staub
Background
Our study aims to enhance future pandemic preparedness by integrating lessons from historical pandemics, focusing on the multidimensional analysis of past outbreaks. It addresses the gap in existing modelling studies by combining various pandemic parameters in a comprehensive setting. Using Zurich as a case study, we seek a deeper understanding of pandemic dynamics to inform future scenarios.
Data and methods
We use newly digitized weekly aggregated epidemic/pandemic time series (incidence, hospitalisations, mortality and sickness absences from work) to retrospectively model the 1918–1920 pandemic in Zurich and investigate how different parameters correspond, how transmissibility changed during the different waves, and how public health interventions were associated with changes in these pandemic parameters.
Results
In general, the various time series show a good temporal correspondence, but differences in their expression can also be observed. The first wave in the summer of 1918 did lead to illness, absence from work and hospitalisations, but to a lesser extent to increased mortality. In contrast, the second, longest and strongest wave in the autumn/winter of 1918 also led to greatly increased (excess) mortality in addition to the burden of illness. The later wave in the first months of 1920 was again associated with an increase in all pandemic parameters. Furthermore, we can see that public health measures such as bans on gatherings and school closures were associated with a decrease in the course of the pandemic, while the lifting or non-compliance with these measures was associated with an increase of reported cases.
Discussion
Our study emphasizes the need to analyse a pandemic's disease burden comprehensively, beyond mortality. It highlights the importance of considering incidence, hospitalizations, and work absences as distinct but related aspects of disease impact. This approach reveals the nuanced dynamics of a pandemic, especially crucial during multi-wave outbreaks.
{"title":"Retrospective modelling of the disease and mortality burden of the 1918–1920 influenza pandemic in Zurich, Switzerland","authors":"Ella Ziegler , Katarina L. Matthes , Peter W. Middelkamp , Verena J. Schuenemann , Christian L. Althaus , Frank Rühli , Kaspar Staub","doi":"10.1016/j.epidem.2025.100813","DOIUrl":"10.1016/j.epidem.2025.100813","url":null,"abstract":"<div><h3>Background</h3><div>Our study aims to enhance future pandemic preparedness by integrating lessons from historical pandemics, focusing on the multidimensional analysis of past outbreaks. It addresses the gap in existing modelling studies by combining various pandemic parameters in a comprehensive setting. Using Zurich as a case study, we seek a deeper understanding of pandemic dynamics to inform future scenarios.</div></div><div><h3>Data and methods</h3><div>We use newly digitized weekly aggregated epidemic/pandemic time series (incidence, hospitalisations, mortality and sickness absences from work) to retrospectively model the 1918–1920 pandemic in Zurich and investigate how different parameters correspond, how transmissibility changed during the different waves, and how public health interventions were associated with changes in these pandemic parameters.</div></div><div><h3>Results</h3><div>In general, the various time series show a good temporal correspondence, but differences in their expression can also be observed. The first wave in the summer of 1918 did lead to illness, absence from work and hospitalisations, but to a lesser extent to increased mortality. In contrast, the second, longest and strongest wave in the autumn/winter of 1918 also led to greatly increased (excess) mortality in addition to the burden of illness. The later wave in the first months of 1920 was again associated with an increase in all pandemic parameters. Furthermore, we can see that public health measures such as bans on gatherings and school closures were associated with a decrease in the course of the pandemic, while the lifting or non-compliance with these measures was associated with an increase of reported cases.</div></div><div><h3>Discussion</h3><div>Our study emphasizes the need to analyse a pandemic's disease burden comprehensively, beyond mortality. It highlights the importance of considering incidence, hospitalizations, and work absences as distinct but related aspects of disease impact. This approach reveals the nuanced dynamics of a pandemic, especially crucial during multi-wave outbreaks.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100813"},"PeriodicalIF":3.0,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014804","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 : 2024-12-25DOI: 10.1016/j.epidem.2024.100810
Evan L. Ray , Yijin Wang , Russell D. Wolfinger , Nicholas G. Reich
Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC’s National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble model that combines two machine learning models using gradient boosting for quantile regression based on different feature sets with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. In each week of the influenza season, these models produced quantiles of a predictive distribution of influenza hospital admissions in each state for the current week and the following three weeks; the ensemble prediction was computed by averaging these quantile predictions. Flusion emerged as the top-performing model in the CDC’s influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion’s success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and multiple locations. These results indicate the value of sharing information across multiple locations and surveillance signals, especially when doing so adds to the pool of available training data.
{"title":"Flusion: Integrating multiple data sources for accurate influenza predictions","authors":"Evan L. Ray , Yijin Wang , Russell D. Wolfinger , Nicholas G. Reich","doi":"10.1016/j.epidem.2024.100810","DOIUrl":"10.1016/j.epidem.2024.100810","url":null,"abstract":"<div><div>Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC’s National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble model that combines two machine learning models using gradient boosting for quantile regression based on different feature sets with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. In each week of the influenza season, these models produced quantiles of a predictive distribution of influenza hospital admissions in each state for the current week and the following three weeks; the ensemble prediction was computed by averaging these quantile predictions. Flusion emerged as the top-performing model in the CDC’s influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion’s success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and multiple locations. These results indicate the value of sharing information across multiple locations and surveillance signals, especially when doing so adds to the pool of available training data.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100810"},"PeriodicalIF":3.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143014806","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 : 2024-12-16DOI: 10.1016/j.epidem.2024.100811
Oron Madmon, Yair Goldberg
Over three years since the first identified SARS-CoV-2 case was discovered, the role of adolescents and children in spreading the virus remains unclear. Specifically, estimating the relative susceptibility of a child with respect to an adult is still an open question. In our work, we generalize a well-known household model for modeling infectious diseases, to include missing tests. Due to missingness, the likelihood of the generalized model cannot be maximized directly. Thus, we propose an estimation methodology, using a novel EM algorithm, for estimating the MLE in the presence of missing data. We implement the proposed mechanism using R software. Using a simulation study, we illustrate the performance of the proposed estimation methodology compared with the estimation procedure in the complete case. Finally, using the proposed estimation methodology we analyzed a dataset containing SARS-CoV-2 testing results, collected from the city of Bnei Brak, Israel, during the beginning of the pandemic. Using this dataset, we show that adolescents are less susceptible than adults, and children are less susceptible than adolescents.
{"title":"Infectious diseases: Household modeling with missing data","authors":"Oron Madmon, Yair Goldberg","doi":"10.1016/j.epidem.2024.100811","DOIUrl":"10.1016/j.epidem.2024.100811","url":null,"abstract":"<div><div>Over three years since the first identified SARS-CoV-2 case was discovered, the role of adolescents and children in spreading the virus remains unclear. Specifically, estimating the relative susceptibility of a child with respect to an adult is still an open question. In our work, we generalize a well-known household model for modeling infectious diseases, to include missing tests. Due to missingness, the likelihood of the generalized model cannot be maximized directly. Thus, we propose an estimation methodology, using a novel EM algorithm, for estimating the MLE in the presence of missing data. We implement the proposed mechanism using R software. Using a simulation study, we illustrate the performance of the proposed estimation methodology compared with the estimation procedure in the complete case. Finally, using the proposed estimation methodology we analyzed a dataset containing SARS-CoV-2 testing results, collected from the city of Bnei Brak, Israel, during the beginning of the pandemic. Using this dataset, we show that adolescents are less susceptible than adults, and children are less susceptible than adolescents.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100811"},"PeriodicalIF":3.0,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873119","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 : 2024-12-06DOI: 10.1016/j.epidem.2024.100809
Sara N. Levintow , Molly Remch , Emily P. Jones , Justin Lessler , Jessie K. Edwards , Lauren Brinkley-Rubinstein , Dana K. Rice , David L. Rosen , Kimberly A. Powers
Background
The prevention and control of infectious disease outbreaks in carceral settings face unique challenges. Transmission modeling is a powerful tool for understanding and addressing these challenges, but reviews of modeling work in this context pre-date the proliferation of outbreaks in jails and prisons during the SARS-CoV-2 pandemic. We conducted a systematic review of studies using transmission models of respiratory infections in carceral settings before and during the pandemic.
Methods
We searched PubMed, Embase, Scopus, CINAHL, and PsycInfo to identify studies published between 1970 and 2024 that modeled transmission of respiratory infectious diseases in carceral settings. We extracted information on the diseases, populations, and settings modeled; approaches used for parameterizing models and simulating transmission; outcomes of interest and techniques for model calibration, validation, and sensitivity analyses; and types, impacts, and ethical aspects of modeled interventions.
Results
Forty-six studies met eligibility criteria, with transmission dynamics of tuberculosis modeled in 24 (52 %), SARS-CoV-2 in 20 (43 %), influenza in one (2 %), and varicella-zoster virus in one (2 %). Carceral facilities in the United States were the most common focus (15, 33 %), followed by Brazil (8, 17 %). Most studies (36, 80 %) used compartmental models (vs. individual- or agent-based). Tuberculosis studies typically modeled transmission within a single facility, while most SARS-CoV-2 studies simulated transmission in multiple places, including between carceral and community settings. Half of studies fit models to epidemiological data; three validated model predictions. Models were used to estimate past or potential future intervention impacts in 32 (70 %) studies, forecast the status quo (without changing conditions) in six (13 %), and examine only theoretical aspects of transmission in eight (17 %). Interventions commonly involved testing and treatment, quarantine and isolation, and/or facility ventilation. Modeled interventions substantially reduced transmission, but some were not well-defined or did not consider ethical issues.
Conclusion
The pandemic prompted urgent attention to transmission dynamics in jails and prisons, but there has been little modeling of respiratory infections other than SARS-CoV-2 and tuberculosis. Increased attention to calibration, validation, and the practical and ethical aspects of intervention implementation could improve translation of model estimates into tangible benefits for the highly vulnerable populations in carceral settings.
{"title":"Transmission models of respiratory infections in carceral settings: A systematic review","authors":"Sara N. Levintow , Molly Remch , Emily P. Jones , Justin Lessler , Jessie K. Edwards , Lauren Brinkley-Rubinstein , Dana K. Rice , David L. Rosen , Kimberly A. Powers","doi":"10.1016/j.epidem.2024.100809","DOIUrl":"10.1016/j.epidem.2024.100809","url":null,"abstract":"<div><h3>Background</h3><div>The prevention and control of infectious disease outbreaks in carceral settings face unique challenges. Transmission modeling is a powerful tool for understanding and addressing these challenges, but reviews of modeling work in this context pre-date the proliferation of outbreaks in jails and prisons during the SARS-CoV-2 pandemic. We conducted a systematic review of studies using transmission models of respiratory infections in carceral settings before and during the pandemic.</div></div><div><h3>Methods</h3><div>We searched PubMed, Embase, Scopus, CINAHL, and PsycInfo to identify studies published between 1970 and 2024 that modeled transmission of respiratory infectious diseases in carceral settings. We extracted information on the diseases, populations, and settings modeled; approaches used for parameterizing models and simulating transmission; outcomes of interest and techniques for model calibration, validation, and sensitivity analyses; and types, impacts, and ethical aspects of modeled interventions.</div></div><div><h3>Results</h3><div>Forty-six studies met eligibility criteria, with transmission dynamics of tuberculosis modeled in 24 (52 %), SARS-CoV-2 in 20 (43 %), influenza in one (2 %), and varicella-zoster virus in one (2 %). Carceral facilities in the United States were the most common focus (15, 33 %), followed by Brazil (8, 17 %). Most studies (36, 80 %) used compartmental models (vs. individual- or agent-based). Tuberculosis studies typically modeled transmission within a single facility, while most SARS-CoV-2 studies simulated transmission in multiple places, including between carceral and community settings. Half of studies fit models to epidemiological data; three validated model predictions. Models were used to estimate past or potential future intervention impacts in 32 (70 %) studies, forecast the status quo (without changing conditions) in six (13 %), and examine only theoretical aspects of transmission in eight (17 %). Interventions commonly involved testing and treatment, quarantine and isolation, and/or facility ventilation. Modeled interventions substantially reduced transmission, but some were not well-defined or did not consider ethical issues.</div></div><div><h3>Conclusion</h3><div>The pandemic prompted urgent attention to transmission dynamics in jails and prisons, but there has been little modeling of respiratory infections other than SARS-CoV-2 and tuberculosis. Increased attention to calibration, validation, and the practical and ethical aspects of intervention implementation could improve translation of model estimates into tangible benefits for the highly vulnerable populations in carceral settings.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"50 ","pages":"Article 100809"},"PeriodicalIF":3.0,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873088","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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