Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational Study (Preprint) DOI
Dorian Arifi, Bernd Resch, Mauricio Santillana

и другие.

Опубликована: Март 19, 2024

BACKGROUND The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and care experts to implement nonpharmaceutical public interventions, such as stay-at-home orders mask mandates, slow the spread of virus. While these interventions proved essential in controlling transmission, they also caused substantial economic societal costs should therefore be used strategically, particularly when activity is on rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown provide a promising tool for anticipating moments potential crises. However, previous studies early warning capabilities data largely constrained by coarse spatial resolutions or short temporal scopes, limited understanding how local political beliefs may influence capabilities. OBJECTIVE This study aimed assess epidemiological COVID-19 vary over time across US counties differing beliefs. METHODS We classified into 3 clusters, democrat, republican, swing counties, based voting from last 6 federal election cycles. we analyzed consecutive waves (February 2020-April 2022). specifically examined lag between signals surges cases, measuring both number days which preceded cases (temporal lag) correlation their respective series. RESULTS differed clusters waves. On average, 21 republican compared 14.6 democrat 24.2 counties. general, were preceding 5 out all clusters. observed decrease that Furthermore, decline signal strength impact trending topics presented challenges reliability signals. CONCLUSIONS provides valuable insights strengths limitations tool, highlighting can change county-level Thus, findings indicate future systems might benefit accounting addition, declining role need assessed research.

Язык: Английский

Innovations dans la surveillance de la santé publique : un aperçu de l'utilisation novatrice des données et des méthodes d'analyse DOI Creative Commons

Heather Rilkoff,

Shannon Struck,

Chelsea Ziegler

и другие.

Relevé des maladies transmissibles au Canada, Год журнала: 2024, Номер 50(3/4), С. 104 - 114

Опубликована: Апрель 30, 2024

Recevez le RMTC dans votre boîte courriel ABONNEZ-VOUS AUJOURD'HUI Recherche web : RMTC+abonnez-vous Connaître les tendances Recevoir directives en matière de dépistage Être à l'affût des nouveaux vaccins Apprendre sur infections émergentes la table matières directement

Процитировано

0
Harnessing the power of artificial intelligence to combat infectious diseases: Progress, challenges, and future outlook DOI

Hang-Yu Zhou,

Yaling Li,

Jiaying Li

и другие.

The Innovation Medicine, Год журнала: 2024, Номер unknown, С. 100091 - 100091

Опубликована: Янв. 1, 2024

<p>The rapid emergence and global spread of infectious diseases pose significant challenges to public health. In recent years, artificial intelligence (AI) technologies have shown great potential in enhancing our ability prevent, detect, control disease outbreaks. However, as a growing interdisciplinarity field, gap exists between AI scientists biologists, limiting the full this field. This review provides comprehensive overview applications diseases, focusing on progress along four stages outbreaks: pre-pandemic, early pandemic, periodic epidemic stages. We discuss methods detection risk assessment, outbreak surveillance, diagnosis control, understanding pathogenic mechanisms. also propose primary limitations, challenges, solutions associated with tools health contexts while examining crucial considerations for future enhanced implementation. By harnessing power AI, we can develop more precise targeted strategies mitigate burden improve health.</p>

Язык: Английский

Процитировано

0
Nowcasting epidemic trends using hospital- and community-based virologic test data DOI Creative Commons
Tse Yang Lim, Sanjat Kanjilal, Shira Doron

и другие.

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Ноя. 2, 2024

Abstract Epidemiological surveillance typically relies on reported incidence of cases or hospitalizations, which can suffer significant reporting lags, biases and under-ascertainment. Here, we evaluated the potential viral loads measured by RT-qPCR cycle threshold (Ct) values to track epidemic trends. We used SARS-CoV-2 results from hospital testing in Massachusetts, USA, municipal California, simulations identify predictive models covariates that maximize short-term trend prediction accuracy. found Ct value distributions correlated with growth rates under real-world conditions. fitted generalized additive predict log rate direction case using features time-varying population distribution assessed models’ ability dynamics rolling two-week windows. Observed accurately predicted (growth RMSE ∼ 0.039-0.052) (AUC 0.72-0.78). Performance degraded during periods rapidly changing rate. Predictive were robust regimes sample sizes; accounting for immunity symptom status yielded no substantial improvement. Trimming outliers improved performance. These indicate analysis routine PCR tests help monitor trends, complementing traditional metrics.

Язык: Английский

Процитировано

0
How mathematical modelling can inform outbreak response vaccination DOI Creative Commons

Manjari Shankar,

Anna-Maria Hartner, Callum Arnold

и другие.

BMC Infectious Diseases, Год журнала: 2024, Номер 24(1)

Опубликована: Дек. 1, 2024

Abstract Mathematical models are established tools to assist in outbreak response. They help characterise complex patterns disease spread, simulate control options public health authorities decision-making, and longer-term operational financial planning. In the context of vaccine-preventable diseases (VPDs), vaccines one most-cost effective response interventions, with potential avert significant morbidity mortality through timely delivery. Models can contribute design vaccine by investigating importance timeliness, identifying high-risk areas, prioritising use limited supply, highlighting surveillance gaps reporting, determining short- long-term benefits. this review, we examine how have been used inform for 10 VPDs, provide additional insights into challenges modelling, such as data gaps, key vaccine-specific considerations, communication between modellers stakeholders. We illustrate that while policy-oriented response, they only be good them.

Язык: Английский

Процитировано

0
Geosocial Media’s Early Warning Capabilities Across US County-Level Political Clusters: Observational Study (Preprint) DOI
Dorian Arifi, Bernd Resch, Mauricio Santillana

и другие.

Опубликована: Март 19, 2024

BACKGROUND The novel coronavirus disease (COVID-19) sparked significant health concerns worldwide, prompting policy makers and care experts to implement nonpharmaceutical public interventions, such as stay-at-home orders mask mandates, slow the spread of virus. While these interventions proved essential in controlling transmission, they also caused substantial economic societal costs should therefore be used strategically, particularly when activity is on rise. In this context, geosocial media posts (posts with an explicit georeference) have been shown provide a promising tool for anticipating moments potential crises. However, previous studies early warning capabilities data largely constrained by coarse spatial resolutions or short temporal scopes, limited understanding how local political beliefs may influence capabilities. OBJECTIVE This study aimed assess epidemiological COVID-19 vary over time across US counties differing beliefs. METHODS We classified into 3 clusters, democrat, republican, swing counties, based voting from last 6 federal election cycles. we analyzed consecutive waves (February 2020-April 2022). specifically examined lag between signals surges cases, measuring both number days which preceded cases (temporal lag) correlation their respective series. RESULTS differed clusters waves. On average, 21 republican compared 14.6 democrat 24.2 counties. general, were preceding 5 out all clusters. observed decrease that Furthermore, decline signal strength impact trending topics presented challenges reliability signals. CONCLUSIONS provides valuable insights strengths limitations tool, highlighting can change county-level Thus, findings indicate future systems might benefit accounting addition, declining role need assessed research.

Язык: Английский

Процитировано

0