Epidemics最新文献

{"title":"The bridge between two worlds: Global South researchers' journeys through Global North academic training and beyond","authors":"Bimandra A. Djaafara ,&nbsp;Mumbua Mutunga ,&nbsp;Obiora A. Eneanya ,&nbsp;Alpha Forna ,&nbsp;Zulma M. Cucunubá","doi":"10.1016/j.epidem.2025.100864","DOIUrl":"10.1016/j.epidem.2025.100864","url":null,"abstract":"<div><div>International training of Global South researchers represents a strategic investment that yields substantial returns, rather than the traditional “brain drain” framing. This perspective synthesises the experiences of infectious disease epidemiologists from Colombia, Indonesia, Kenya, Nigeria, and Sierra Leone who completed training in Global North institutions between 2015 and 2024. Despite facing challenges, language barriers, and representational pressures, we demonstrate how Global South researchers transform these obstacles into unique strengths that enhance local research capabilities. Our experiences also show that Global South researchers serve as vital bridges between academic worlds, contributing irreplaceable contextual knowledge while building collaborative networks that advance infectious disease epidemiology research regardless of geographic location. We provide four strategic recommendations for better infectious disease epidemiology research ecosystems: 1) creating supportive institutional environments in Global North institutions, 2) building sustainable partnerships that strengthen home institutions, 3) embracing individual agency and responsibility, and 4) strengthening regional collaborations while adapting to evolving global contexts. Our narrative progresses from challenges to empowerment, demonstrating that Global South researchers are valuable contributors essential to advancing infectious disease epidemiology research.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100864"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145410676","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":"Reimagining the serocatalytic model for infectious diseases: A case study of common coronaviruses","authors":"Soren L. Larsen ,&nbsp;Junke Yang ,&nbsp;Huibin Lv ,&nbsp;Yang Wei Huan ,&nbsp;Qi Wen Teo ,&nbsp;Tossapol Pholcharee ,&nbsp;Ruipeng Lei ,&nbsp;Akshita B. Gopal ,&nbsp;Evan K. Shao ,&nbsp;Logan Talmage ,&nbsp;Chris K.P. Mok ,&nbsp;Saki Takahashi ,&nbsp;Alicia N.M. Kraay ,&nbsp;Nicholas C. Wu ,&nbsp;Pamela P. Martinez","doi":"10.1016/j.epidem.2025.100859","DOIUrl":"10.1016/j.epidem.2025.100859","url":null,"abstract":"<div><div>Despite the increased availability of serological data, understanding serodynamics remains challenging. Serocatalytic models, which describe the rate of seroconversion (gain of antibodies) and seroreversion (loss of antibodies) within a population, have traditionally been fit to cross-sectional serological data to capture long-term transmission dynamics. However, a key limitation is their binary assumption on serological status, ignoring heterogeneity in optical density levels, antibody titers, and/or exposure history. Here, we implemented Gaussian mixture models - an established statistical tool - to cross-sectional data in order to characterize serological diversity of seasonal human coronaviruses (sHCoVs) across a wide range of age groups. These methods consistently identified multiple distinct seropositive levels, suggesting that among seropositive individuals, the number of prior exposures or response to infection may vary. We fit adapted, multi-compartment serocatalytic models with different assumptions on exposure history and waning of antibodies. The best fit model for each sHCoV was always one that accounted for host variation in the scale of serological response to infection. These models allowed us to estimate the strength and frequency of serological responses, finding that the time for a seronegative individual to become seropositive ranges from 2.40 to 7.03 years across sHCoVs, and most individuals mount a strong antibody response reflected in high optical density values, skipping lower levels of seropositivity. We find that despite frequent infection and strong serological responses, for all sHCoVs except 229E, individuals are likely to become seronegative again at some point after their first infection. Nonetheless, our results also indicate that by age 22, for each sHCoV the probability of having seroconverted at least once is over 95%. Crucially, our reimagined serocatalytic methods can be flexibly adapted across pathogens, having the potential to be broadly applied beyond this work.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100859"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294198","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":"Forecasting regional COVID-19 hospitalisation in England using ordinal machine learning method","authors":"Haowei Wang ,&nbsp;Kin On Kwok ,&nbsp;Ruiyun Li ,&nbsp;Steven Riley","doi":"10.1016/j.epidem.2025.100856","DOIUrl":"10.1016/j.epidem.2025.100856","url":null,"abstract":"<div><h3>Background</h3><div>The COVID-19 pandemic caused substantial pressure on healthcare, with many systems needing to prepare for and mitigate the consequences of surges in demand caused by multiple overlapping waves of infections. Therefore, public health agencies and health system managers also benefitted from short-term forecasts for respiratory infections that allowed them to manage services. While quantitative forecasts treating hospital admissions as continuous variables existed, many health managers prefer discrete levels of demand, similar to approaches used in weather and flooding. However, effective tools for generating precise sub-national forecasts remained limited.</div></div><div><h3>Methods</h3><div>We forecast regional COVID-19 hospitalisations in England, using the period from March 2020 to December 2021 for training and evaluating predictions using data from January to December 2022. We transform regional admission counts into an ordinal variable using n-tile and n-uniform methods. We further developed a method based on XGBoost, and used previously for influenza, to enable it to exploit the ordering information in ordinal hospital admission levels. We incorporated different types of data as predictors: epidemiological data including weekly region COVID-19 cases and hospital admissions, weather conditions and mobility data for multiple categories of locations. The impact of different discretisation methods and the number of ordinal levels was also considered.</div></div><div><h3>Results</h3><div>We found that mobility data brings about a more substantial improvement in predictive performance than relying only on epidemiological data and the inclusion of weather data. When both weather and mobility data are used in addition to epidemiological data, the results are very similar to models with only epidemiological data and mobility data. These results are robust in terms of the number of levels chosen for the forecast target.</div></div><div><h3>Conclusion</h3><div>Accurate ordinal forecasts of COVID-19 hospitalisations were obtained using XGBoost and mobility data. While uniform ordinal levels showed higher apparent accuracy, we recommend n-tile ordinal levels which contain far richer information.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100856"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201860","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":"Optimisation of wastewater surveillance for COVID-19 after resumption of normalcy from the pandemic: A case of Hong Kong","authors":"Eugene SK LO,&nbsp;Serana CY SO,&nbsp;LT WONG,&nbsp;Kirran N. MOHAMMAD,&nbsp;KY LAW,&nbsp;KS CHAN,&nbsp;Shirley WY TSANG,&nbsp;Dawin LO,&nbsp;KH KUNG,&nbsp;Albert KW AU,&nbsp;SK CHUANG","doi":"10.1016/j.epidem.2025.100853","DOIUrl":"10.1016/j.epidem.2025.100853","url":null,"abstract":"<div><div>Wastewater surveillance (WWS) was critical to Hong Kong’s COVID-19 response, providing early warning indicators and enabling targeted measures to control the epidemic in the city during the pandemic. As the approach to COVID-19 transitioned from containment to long-term management, maintaining the WWS programme became challenging owing to financial limitations. This article chronicles our efforts to optimize the programme to guarantee its long-term sustainability while preserving its efficacy in tracking disease trends and detecting novel variants. Prior to optimization, our WWS programme gathered samples from 120 locations weekly, encompassing 80 % of the population. Drawing from our experience, we examined several optimization measures, such as decreasing frequency of sampling and altering testing procedures. Nonetheless, these methods were deemed impractical owing to operational and technical difficulties. Ultimately, we determined that a reduction in sampling sites was the most viable method. Statistical analyses utilizing data from April 2023 to March 2024 corroborated this methodology, indicating that despite an 85 % decrease in sample locations (from 120 to 18), the surveillance data retained a high degree of reliability (R² &gt; 0.97) compared to the original model. This optimized methodology decreased expenses by about 80 % while maintaining data reliability. By disseminating our methodology and findings, we aim to provide useful information that may aid other jurisdictions in establishing cost-effective WWS systems as they confront analogous difficulties globally.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100853"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050262","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":"A binary prototype for time-series surveillance and intervention","authors":"Jason Olejarz ,&nbsp;Till Hoffmann ,&nbsp;Alex Zapf ,&nbsp;Douaa Mugahid ,&nbsp;Ross Molinaro ,&nbsp;Chadwick Brown ,&nbsp;Artem Boltyenkov ,&nbsp;Taras Dudykevych ,&nbsp;Ankit Gupta ,&nbsp;Marc Lipsitch ,&nbsp;Rifat Atun ,&nbsp;Jukka-Pekka Onnela ,&nbsp;Sarah Fortune ,&nbsp;Rangarajan Sampath ,&nbsp;Yonatan H. Grad","doi":"10.1016/j.epidem.2025.100866","DOIUrl":"10.1016/j.epidem.2025.100866","url":null,"abstract":"<div><div>Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, <span><math><mi>c</mi></math></span>, whenever the system is abnormal and no action is taken, or an immediate cost, <span><math><mi>k</mi></math></span>, with action, where <span><math><mrow><mi>k</mi><mo>&lt;</mo><mi>c</mi></mrow></math></span>. If action costs are too high, then surveillance is detrimental, and intervention should never occur. If action costs are sufficiently low, then surveillance is detrimental, and intervention should always occur. Only when action costs are intermediate and surveillance costs are sufficiently low is surveillance beneficial. Our equations provide a framework for assessing which approach may apply under a range of scenarios and, if surveillance is warranted, facilitate methodical classification of intervention strategies. Our model thus offers a conceptual basis for designing real-world public health surveillance systems.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100866"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528693","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":"Artificial intelligence for health security in Africa: Benefits, risks and opportunities","authors":"Claire J. Standley ,&nbsp;J. Gabrielle Breugelmans ,&nbsp;Amol Chaudhari ,&nbsp;Neil Cherian ,&nbsp;Sabrina Chwalek ,&nbsp;Arminder Deol ,&nbsp;Janan Dietrich ,&nbsp;Lora du Moulin ,&nbsp;Geoffrey Otim ,&nbsp;Wilmot James ,&nbsp;Stefan Kloth ,&nbsp;Sana Masmoudi ,&nbsp;Nicaise Ndembi ,&nbsp;Nqobile Ndlovu ,&nbsp;Danny Scarponi ,&nbsp;Franz Schnetzinger ,&nbsp;Molly Shapiro ,&nbsp;Andrew Hebbeler","doi":"10.1016/j.epidem.2025.100870","DOIUrl":"10.1016/j.epidem.2025.100870","url":null,"abstract":"<div><div>Artificial intelligence (AI) provides paradigm-shifting opportunities to accelerate epidemic preparedness and response and ensure health security. Such benefits may be particularly applicable to countries in Africa, which have to date struggled to meet compliance obligations under international health security frameworks. Here, we build on discussions that took place at the March 2024 Health Security Partnership for Africa workshop in Addis Ababa, Ethiopia, to describe potential applications of AI-enabled approaches to accelerate activities throughout the preparedness ecosystem, with a particular focus on the rapid development and deployment of novel vaccines in support of the 100 Days Mission, focusing on Africa. We also consider the risks and barriers that may challenge successful deployment of AI for health security in African settings, and opportunities to elevate African leadership on governance and implementation.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100870"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145574407","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":"Integrating macroeconomic and public health impacts in social planning policies for pandemic response","authors":"Ofer Cornfeld ,&nbsp;Kaicheng Niu ,&nbsp;Oded Neeman ,&nbsp;Michael Roswell ,&nbsp;Gabi Steinbach ,&nbsp;Stephen J. Beckett ,&nbsp;Yorai Wardi ,&nbsp;Joshua S. Weitz ,&nbsp;Eran Yashiv","doi":"10.1016/j.epidem.2025.100873","DOIUrl":"10.1016/j.epidem.2025.100873","url":null,"abstract":"<div><div>Infectious disease outbreaks with pandemic potential present challenges for mitigation and control. Policymakers must reduce disease-associated morbidity and mortality while also minimizing socioeconomic costs of interventions. At present, robust decision frameworks that integrate epidemic and macroeconomic dynamics to inform policy choices, given uncertainty in the current and future state of the outbreak and economic activity, are not widely available. In this study, we propose and analyze an economic-epidemic model to identify robust planning policies that limit epidemic impacts while maintaining economic activity. We compare alternative fixed, dynamic open-loop optimal control, and feedback control policies via a welfare loss framework. We find that open-loop policies that adjust employment dynamically while maintaining a flat epidemic curve outperform fixed employment reduction policies. However, open-loop policies are highly sensitive to misestimation of parameters associated with intrinsic disease strength and feedback between economic activity and transmission, leading to potentially significant increases in welfare loss. In contrast, feedback control policies guided by open-loop dynamical targets of the time-varying reproduction number perform near-optimally when parameters are well-estimated, while significantly outperforming open-loop policies whenever disease transmission and population-scale behavioral response parameters are misestimated — as they inevitably are. Our study provides a template for integrating principled economic models with epidemic scenarios to identify policy vulnerabilities and expand policy options in preparation for future pandemics. Across disease scenarios, we show that policies that temporarily limit economic activity and disease transmission reduce both disease-driven mortality and cumulative loss of economic activity. Our study suggests that future preparedness depends on feasible, robust, and adaptive policies and can help avoid false dichotomies in choosing between public health and economic outcomes.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"53 ","pages":"Article 100873"},"PeriodicalIF":2.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145694157","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":"Transmission dynamics of Norovirus GII and Enterovirus in Switzerland during the COVID-19 pandemic (2021–2022) as evidenced in wastewater","authors":"Jana S. Huisman ,&nbsp;Shotaro Torii ,&nbsp;Htet Kyi Wynn ,&nbsp;Charles Gan ,&nbsp;Irene K. Voellmy ,&nbsp;Michael Huber ,&nbsp;Timothy R. Julian ,&nbsp;Tamar Kohn","doi":"10.1016/j.epidem.2025.100851","DOIUrl":"10.1016/j.epidem.2025.100851","url":null,"abstract":"<div><div>Noroviruses and enteroviruses are major causes of endemic gastrointestinal disease associated with substantial disease burden. However, viral gastroenteritis is often diagnosed based on symptoms, with etiology infrequently tested or reported, so little information exists on community-level transmission dynamics. In this study, we demonstrate that norovirus (NoV) genogroup II and enterovirus (EV) viral loads in wastewater reveal transmission dynamics of these viruses. We report NoV and EV concentrations in wastewater from 363 samples between December 5 2020 and October 10 2022 (sampled every second day). Virus concentrations in wastewater were low during 2021, and increased in 2022. Wastewater recapitulated periods of increased clinical cases, and also identified silent waves of transmission. We used the measured wastewater loads to estimate the effective reproductive number (R_e). The R_e for both NoV and EV peaked between 1.1 and 1.2. However, the usual seasonality of NoV transmission was upended by non-pharmaceutical interventions implemented to mitigate the COVID-19 pandemic, leading to correlated transmission dynamics of NoV GII and EV during 2021–2022. This highlights the use of wastewater to understand transmission dynamics of endemic enteric viruses and estimate relevant epidemiological parameters, including R_e.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100851"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144879110","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":"A Cluster-Aggregate-Pool (CAP) ensemble algorithm for improved forecast performance of influenza-like illness","authors":"Ningxi Wei ,&nbsp;Xinze Zhou ,&nbsp;Wei-Min Huang ,&nbsp;Thomas McAndrew","doi":"10.1016/j.epidem.2025.100832","DOIUrl":"10.1016/j.epidem.2025.100832","url":null,"abstract":"<div><div>Seasonal influenza causes on average 425,000 hospitalizations and 32,000 deaths per year in the United States. Forecasts of influenza-like illness (ILI) — a surrogate for the proportion of patients infected with influenza — support public health decision making. The goal of an ensemble forecast of ILI is to increase accuracy and calibration compared to individual forecasts and to provide a single, cohesive prediction of future influenza. However, an ensemble may be composed of models that produce similar forecasts, causing issues with ensemble forecast performance and non-identifiability. To improve upon the above issues we propose a novel Cluster-Aggregate-Pool or ‘CAP’ ensemble algorithm that first groups together individual forecasts into clusters, aggregates forecasts that belong to the same cluster into a single forecast (called a cluster forecast), and then pools together cluster forecasts via a linear pool. We evaluated this algorithm on a benchmark dataset of 7 seasons of ILI plus forecasts generated by 27 individual models as part of the FluSight project. When compared to a non-CAP approach, we find that a CAP ensemble improves calibration by approximately 10% while maintaining similar accuracy to non-CAP alternatives. In addition, our CAP algorithm (i) generalizes past ensemble work associated with influenza forecasting and introduces a framework for future ensemble work, (ii) automatically accounts for missing forecasts from individual models, (iii) allows public health officials to participate in the ensemble by assigning individual models to clusters, and (iv) provide an additional signal about when peak influenza may be near.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100832"},"PeriodicalIF":3.0,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297581","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":"Investigating the impact of edge weight selection on the pig trade network topology","authors":"Gavrila A. Puspitarani ,&nbsp;Yan-Shin Jackson Liao ,&nbsp;Reinhard Fuchs ,&nbsp;Amélie Desvars-Larrive","doi":"10.1016/j.epidem.2025.100849","DOIUrl":"10.1016/j.epidem.2025.100849","url":null,"abstract":"<div><div>Traceability of animal movements and robust surveillance are crucial for prevention and control of animal diseases. While network analysis has emerged as a powerful tool for identifying higher-risk holdings through centrality metrics, its effectiveness depends on two methodological choices: (1) edge-weighting schemes (movement frequency vs. animal volume) and (2) centrality metric selection. This study investigates how alternative edge-weighting approaches (frequency vs. volume) influence network topology and node centrality rankings in a pig movement network.</div><div>Using 2021 pig movement data from Upper Austria (5,766 holdings; 92,914 movements), we: (1) quantify how edge-weighting schemes (frequency vs. volume) affect network topology and community structure, and (2) evaluate node ranking robustness across three centrality metrics (strength, betweenness, closeness) against epidemic simulation rankings. Our analysis reveals distinct edge weight distributions: frequency-based network exhibited a bimodal pattern, while volume-based was more uniform. We observed strong positive correlations (<span><math><mi>τ</mi></math></span> <span><math><mo>&gt;</mo></math></span> 0.42–0.84; <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>) in node rankings across all centrality metrics (strength, closeness, betweenness), with consistent patterns observed both: (i) between frequency- and volume-weighted networks, and (ii) within each network representation. Strength centrality exhibited the highest correlation with the simulation-based rankings, particularly for the top 5% highest-ranked nodes (<span><math><mrow><mi>τ</mi><mi>b</mi></mrow></math></span> <span><math><mo>=</mo></math></span> 0.51 for frequency-based and <span><math><mrow><mi>τ</mi><mi>b</mi></mrow></math></span> <span><math><mo>=</mo></math></span> 0.5 for volume-based). These findings highlight that strength centrality provides a computationally efficient and field-practical alternative to epidemic simulations for identifying high-risk holdings. This enables resource-efficient, data-driven surveillance while maintaining epidemiological relevance.</div></div>","PeriodicalId":49206,"journal":{"name":"Epidemics","volume":"52 ","pages":"Article 100849"},"PeriodicalIF":2.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144907267","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}