Epidemics最新文献

{"title":"Assessing the utility of COVID-19 case reports as a leading indicator for hospitalization forecasting in the United States","authors":"Nicholas G. Reich ,&nbsp;Yijin Wang ,&nbsp;Meagan Burns ,&nbsp;Rosa Ergas ,&nbsp;Estee Y. Cramer ,&nbsp;Evan L. Ray","doi":"10.1016/j.epidem.2023.100728","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100728","url":null,"abstract":"<div><p>Identifying data streams that can consistently improve the accuracy of epidemiological forecasting models is challenging. Using models designed to predict daily state-level hospital admissions due to COVID-19 in California and Massachusetts, we investigated whether incorporating COVID-19 case data systematically improved forecast accuracy. Additionally, we considered whether using case data aggregated by date of test or by date of report from a surveillance system made a difference to the forecast accuracy. Evaluating forecast accuracy in a test period, after first having selected the best-performing methods in a validation period, we found that overall the difference in accuracy between approaches was small, especially at forecast horizons of less than two weeks. However, forecasts from models using cases aggregated by test date showed lower accuracy at longer horizons and at key moments in the pandemic, such as the peak of the Omicron wave in January 2022. Overall, these results highlight the challenge of finding a modeling approach that can generate accurate forecasts of outbreak trends both during periods of relative stability and during periods that show rapid growth or decay of transmission rates. While COVID-19 case counts seem to be a natural choice to help predict COVID-19 hospitalizations, in practice any benefits we observed were small and inconsistent.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100728"},"PeriodicalIF":3.8,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000646/pdfft?md5=501f8c5a916c41a194955789cddb3e5e&pid=1-s2.0-S1755436523000646-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"109127541","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}

{"title":"Estimation of waning vaccine effectiveness from population-level surveillance data in multi-variant epidemics","authors":"Hiroaki Murayama ,&nbsp;Akira Endo ,&nbsp;Shouto Yonekura","doi":"10.1016/j.epidem.2023.100726","DOIUrl":"10.1016/j.epidem.2023.100726","url":null,"abstract":"<div><p>Monitoring time-varying vaccine effectiveness (e.g., due to waning of immunity and the emergence of novel variants) provides crucial information for outbreak control. Existing studies of time-varying vaccine effectiveness have used individual-level data, most importantly dates of vaccination and variant classification, which are often not available in a timely manner or from a wide range of population groups. We present a novel Bayesian framework for estimating the waning of variant-specific vaccine effectiveness in the presence of multi-variant circulation from population-level surveillance data. Applications to simulated outbreaks and the COVID-19 epidemic in Japan are also presented. Our results show that variant-specific waning vaccine effectiveness estimated from population-level surveillance data could approximately reproduce the estimates from previous test-negative design studies, allowing for rapid, if crude, assessment of the epidemic situation before fine-scale studies are made available.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100726"},"PeriodicalIF":3.8,"publicationDate":"2023-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000622/pdfft?md5=ea726add6890caef681ab98cd068361d&pid=1-s2.0-S1755436523000622-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523072","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}

{"title":"Modelling flock heterogeneity in the transmission of peste des petits ruminants virus and its impact on the effectiveness of vaccination for eradication","authors":"Bethan Savagar ,&nbsp;Bryony A. Jones ,&nbsp;Mark Arnold ,&nbsp;Martin Walker ,&nbsp;Guillaume Fournié","doi":"10.1016/j.epidem.2023.100725","DOIUrl":"10.1016/j.epidem.2023.100725","url":null,"abstract":"<div><p>Peste des petits ruminants (PPR) is an acute infectious disease of small ruminants targeted for global eradication by 2030. The Global Strategy for Control and Eradication (GSCE) recommends mass vaccination targeting 70% coverage of small ruminant populations in PPR-endemic regions. These small ruminant populations are diverse with heterogeneous mixing patterns that may influence PPR virus (PPRV) transmission dynamics. This paper evaluates the impact of heterogeneous mixing on (i) PPRV transmission and (ii) the likelihood of different vaccination strategies achieving PPRV elimination, including the GSCE recommended strategy. We develop models simulating heterogeneous transmission between hosts, including a metapopulation model of PPRV transmission between villages in lowland Ethiopia fitted to serological data. Our results demonstrate that although heterogeneous mixing of small ruminant populations increases the instability of PPRV transmission—increasing the chance of fadeout in the absence of intervention—a vaccination coverage of 70% may be insufficient to achieve elimination if high-risk populations are not targeted. Transmission may persist despite very high vaccination coverage (&gt;90% small ruminants) if vaccination is biased towards more accessible but lower-risk populations such as sedentary small ruminant flocks. These results highlight the importance of characterizing small ruminant mobility patterns and identifying high-risk populations for vaccination and support a move towards targeted, risk-based vaccination programmes in the next phase of the PPRV eradication programme. Our modelling approach also illustrates a general framework for incorporating heterogeneous mixing patterns into models of directly transmitted infectious diseases where detailed contact data are limited. This study improves understanding of PPRV transmission and elimination in heterogeneous small ruminant populations and should be used to inform and optimize the design of PPRV vaccination programmes.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100725"},"PeriodicalIF":3.8,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000610/pdfft?md5=5095bc68ce3f1a1597706b90994e1832&pid=1-s2.0-S1755436523000610-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71487939","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}

{"title":"Mathematical methods for scaling from within-host to population-scale in infectious disease systems","authors":"James W.G. Doran ,&nbsp;Robin N. Thompson ,&nbsp;Christian A. Yates ,&nbsp;Ruth Bowness","doi":"10.1016/j.epidem.2023.100724","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100724","url":null,"abstract":"<div><p>Mathematical modellers model infectious disease dynamics at different scales. Within-host models represent the spread of pathogens inside an individual, whilst between-host models track transmission between individuals. However, pathogen dynamics at one scale affect those at another. This has led to the development of multiscale models that connect within-host and between-host dynamics. In this article, we systematically review the literature on multiscale infectious disease modelling according to PRISMA guidelines, dividing previously published models into five categories governing their methodological approaches (Garira (2017)), explaining their benefits and limitations. We provide a primer on developing multiscale models of infectious diseases.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100724"},"PeriodicalIF":3.8,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000609/pdfft?md5=42f11d0050552382ff9757df7e1a40db&pid=1-s2.0-S1755436523000609-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134654324","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}

{"title":"Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19","authors":"James M. Azam ,&nbsp;Xiaoxi Pang ,&nbsp;Elisha B. Are ,&nbsp;Juliet R.C. Pulliam ,&nbsp;Matthew J. Ferrari","doi":"10.1016/j.epidem.2023.100720","DOIUrl":"10.1016/j.epidem.2023.100720","url":null,"abstract":"<div><h3>Background:</h3><p>Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination.</p></div><div><h3>Methods:</h3><p>We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices.</p></div><div><h3>Results:</h3><p>Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013–2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation.</p></div><div><h3>Conclusion:</h3><p>The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100720"},"PeriodicalIF":3.8,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000567/pdfft?md5=3026204558e1fdbce504ec75af88ca46&pid=1-s2.0-S1755436523000567-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72015916","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}

{"title":"Trypanosoma cruzi infection in dogs along the US-Mexico border: R0 changes with vector species composition","authors":"Luis Fernando Chaves ,&nbsp;Alyssa C. Meyers ,&nbsp;Carolyn L. Hodo ,&nbsp;John P. Sanders ,&nbsp;Rachel Curtis-Robles ,&nbsp;Gabriel L. Hamer ,&nbsp;Sarah A. Hamer","doi":"10.1016/j.epidem.2023.100723","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100723","url":null,"abstract":"<div><p>Infection with <em>Trypanosoma cruzi,</em> etiological agent of Chagas disease, is common in US government working dogs along the US-Mexico border. This 3145 km long border comprises four states: Texas (TX), New Mexico (NM), Arizona (AZ) and California (CA) with diverse ecosystems and several triatomine (a.k.a., kissing bug) species, primary vectors of <em>T. cruzi</em> in this region. The kissing bug (Heteroptera: Reduviidae) community ranging from CA to TX includes <em>Triatoma protracta</em> (Uhler), <em>Triatoma recurva</em> (Stål) and <em>Triatoma rubida</em> (Uhler) and becomes dominated by <em>Triatoma gerstaeckeri</em> Stål in TX. Here, we ask if <em>T. cruzi</em> infection dynamics in dogs varies along this border region, potentially reflecting changes in vector species and their vectorial capacity. Using reversible catalytic models of infection, where seropositivity can be lost, we estimated an <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> (Estimate ± S.E.) of 1.192 ± 0.084 for TX and NM. In contrast, seropositivity decayed to zero as dogs aged in AZ and CA. These results suggest that dogs are likely infected by <em>T. cruzi</em> during their training in western TX, with a force of infection large enough for keeping <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> above 1, i.e., the disease endemically established, in TX and NM. In AZ and CA, a lower force of infection, probably associated with different vector species communities and associated vectorial capacity and/or different lineages of <em>T. cruzi</em>, results in dogs decreasing their seropositivity with age.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100723"},"PeriodicalIF":3.8,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000592/pdfft?md5=57f445b1b533dc6f207553a8bb34d8fe&pid=1-s2.0-S1755436523000592-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92065947","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}

{"title":"A nationwide lockdown and deaths due to COVID-19 in the Indian subcontinent","authors":"Amit N. Sawant,&nbsp;Mats J. Stensrud","doi":"10.1016/j.epidem.2023.100722","DOIUrl":"https://doi.org/10.1016/j.epidem.2023.100722","url":null,"abstract":"<div><p>During the COVID-19 pandemic, the effects of nationwide lockdowns on health outcomes have been widely studied in Western, developed countries. However, the effects of lockdowns in emerging and developing countries are largely unknown. We used data from India and Bangladesh to study the effect of nationwide restrictions on public movement in Bangladesh in April 2021 on health outcomes, specifically COVID-19 incidence and mortality. India and Bangladesh had nearly identical development of the COVID-19 Delta wave the weeks before the lockdown. We leveraged longitudinal data from the pre- and post-intervention period in both countries in a structural causal model, suggesting that the reported deaths in Bangladesh due to COVID-19 would have been <span><math><mrow><mo>∼</mo><mn>117</mn><mtext>%</mtext></mrow></math></span> higher (95% PI: 72%–170%) in April 2021 had there been fewer restrictions. Further, we used population mobility data from Google to study behavioural changes in the two countries, supporting the hypothesis that the intervention had substantial effects on the mobility trends of the Bangladeshi population, which in turn reduced the number of COVID-19 deaths.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100722"},"PeriodicalIF":3.8,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000580/pdfft?md5=7adbe1b1f5b16df548daebadb5d9bf87&pid=1-s2.0-S1755436523000580-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92065946","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}

{"title":"Model-based estimates of chikungunya epidemiological parameters and outbreak risk from varied data types","authors":"Alexander D. Meyer ,&nbsp;Sandra Mendoza Guerrero ,&nbsp;Natalie E. Dean ,&nbsp;Kathryn B. Anderson ,&nbsp;Steven T. Stoddard ,&nbsp;T. Alex Perkins","doi":"10.1016/j.epidem.2023.100721","DOIUrl":"10.1016/j.epidem.2023.100721","url":null,"abstract":"<div><p>Assessing the factors responsible for differences in outbreak severity for the same pathogen is a challenging task, since outbreak data are often incomplete and may vary in type across outbreaks (e.g., daily case counts, serology, cases per household). We propose that outbreaks described with varied data types can be directly compared by using those data to estimate a common set of epidemiological parameters. To demonstrate this for chikungunya virus (CHIKV), we developed a realistic model of CHIKV transmission, along with a Bayesian inference method that accommodates any type of outbreak data that can be simulated. The inference method makes use of the fact that all data types arise from the same transmission process, which is simulated by the model. We applied these tools to data from three real-world outbreaks of CHIKV in Italy, Cambodia, and Bangladesh to estimate nine model parameters. We found that these populations differed in several parameters, including pre-existing immunity and house-to-house differences in mosquito activity. These differences resulted in posterior predictions of local CHIKV transmission risk that varied nearly fourfold: 16% in Italy, 28% in Cambodia, and 62% in Bangladesh. Our inference method and model can be applied to improve understanding of the epidemiology of CHIKV and other pathogens for which outbreaks are described with varied data types.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100721"},"PeriodicalIF":3.8,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1755436523000579/pdfft?md5=ec8a1dcbc137c07987bb7e1df14765ae&pid=1-s2.0-S1755436523000579-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"61565655","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}

{"title":"Epidemiological characteristics and dynamic transmissions of COVID-19 pandemics in Chinese mainland: A trajectory clustering perspective analysis","authors":"Jingfeng Chen ,&nbsp;Shuaiyin Chen ,&nbsp;Guangcai Duan ,&nbsp;Teng Zhang ,&nbsp;Haitao Zhao ,&nbsp;Zhuoqing Wu ,&nbsp;Haiyan Yang ,&nbsp;Suying Ding","doi":"10.1016/j.epidem.2023.100719","DOIUrl":"10.1016/j.epidem.2023.100719","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background&lt;/h3&gt;&lt;p&gt;The corona virus disease 2019 (COVID-19) pandemic has spread to more than 210 countries and regions around the world, with different characteristics recorded depending on the location. A systematic summarization of COVID-19 outbreaks that occurred during the “dynamic zero-COVID” policy period in Chinese mainland had not been previously conducted. In-depth mining of the big data from the past two years of the COVID-19 pandemics must be performed to clarify their epidemiological characteristics and dynamic transmissions.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;p&gt;Trajectory clustering was used to group epidemic and time-varying reproduction number (Rt) curves of mass outbreaks into different models and reveal the epidemiological characteristics and dynamic transmissions of COVID-19. For the selected single-peak epidemic curves, we constructed a peak-point judgment model based on the dynamic slope and adopted a single-peak fitting model to identify the key time points and peak parameters. Finally, we developed an extreme gradient boosting-based prediction model for peak infection cases based on the total number of infections on the first 3, 5, and 7 days of the initial average incubation period.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;p&gt;(1) A total of 7 52298 cases, including 587 outbreaks in 251 cities in Chinese mainland between June 11, 2020, and June 29, 2022, were collected, and the first wave of COVID-19 outbreaks was excluded. Excluding the Shanghai outbreak in 2022, the 586 remaining outbreaks resulted in 1 25425 infections, with an infection rate of 4.21 per 1 00000 individuals. The number of outbreaks varied based on location, season, and temperature.&lt;/p&gt;&lt;p&gt;(2) Trajectory clustering analysis showed that 77 epidemic curves were divided into four patterns, which were dominated by two single-peak clustering patterns (63.3%). A total of 77 Rt curves were grouped into seven patterns, with the leading patterns including four downward dynamic transmission patterns (74.03%). These curves revealed that the interval from peak to the point where the Rt value dropped below 1 was approximately 5 days.&lt;/p&gt;&lt;p&gt;(3) The peak-point judgment model achieved a better result in the area under the curve (0.96, 95% confidence interval = 0.90–1.00). The single-peak fitting results on the epidemic curves indicated that the interval from the slow-growth point to the sharp-decline point was approximately 4–6 days in more than 50% of mass outbreaks.&lt;/p&gt;&lt;p&gt;(4) The peak-infection-case prediction model exhibited the superior clustering results of epidemic and Rt curves compared with the findings without grouping.&lt;/p&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;p&gt;Overall, our findings suggest the variation in the infection rates during the “dynamic zero-COVID” policy period based on the geographic division, level of economic development, seasonal division, and temperature. Trajectory clustering can be a useful tool for discovering epidemiological characteristics and dynamic tran","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100719"},"PeriodicalIF":3.8,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41144590","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}

{"title":"Differential contagiousness of respiratory disease across the United States","authors":"Abhishek Mallela ,&nbsp;Yen Ting Lin ,&nbsp;William S. Hlavacek","doi":"10.1016/j.epidem.2023.100718","DOIUrl":"10.1016/j.epidem.2023.100718","url":null,"abstract":"<div><p>The initial contagiousness of a communicable disease within a given population is quantified by the basic reproduction number, <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span>. This number depends on both pathogen and population properties. On the basis of compartmental models that reproduce Coronavirus Disease 2019 (COVID-19) surveillance data, we used Bayesian inference and the next-generation matrix approach to estimate region-specific <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> values for 280 of 384 metropolitan statistical areas (MSAs) in the United States (US), which account for 95% of the US population living in urban areas and 82% of the total population. We focused on MSA populations after finding that these populations were more uniformly impacted by COVID-19 than state populations. Our maximum a posteriori (MAP) estimates for <span><math><msub><mrow><mi>R</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> range from 1.9 to 7.7 and quantify the relative susceptibilities of regional populations to spread of respiratory diseases.</p></div><div><h3>One-Sentence Summary</h3><p>Initial contagiousness of Coronavirus Disease 2019 varied over a 4-fold range across urban areas of the United States.</p></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"45 ","pages":"Article 100718"},"PeriodicalIF":3.8,"publicationDate":"2023-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41140424","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}