Space-time cluster detection techniques for infectious diseases: A systematic review.

BACKGROUND: Public health organizations have increasingly harnessed geospatial technologies for disease surveillance, health services allocation, and targeting place-based health promotion initiatives. METHODS: We conducted a systematic review around the theme of space-time clustering detection techniques for infectious diseases using PubMed, Web of Science, and Scopus. Two reviewers independently determined inclusion and exclusion. RESULTS: Of 2,887 articles identified, 354 studies met inclusion criteria, the majority of which were application papers. Studies of airborne diseases were dominant, followed by vector-borne diseases. Most research used aggregated data instead of point data, and a significant proportion of articles used a repetition of a spatial clustering method, instead of using a "true" space-time detection approach, potentially leading to the detection of false positives. Noticeably, most articles did not make their data available, limiting replicability. CONCLUSION: This review underlines recent trends in the application of space-time clustering methods to the field of infectious disease, with a rapid increase during the COVID-19 pandemic.

Utilizing prospective space-time scan statistics to discover the dynamics of coronavirus disease 2019 clusters in the State of São Paulo, Brazil.

BACKGROUND: The number of deaths and people infected with coronavirus disease 2019 (COVID-19) in Brazil has steadily increased in the first few months of the pandemic. Despite the underreporting of coronavirus cases by government agencies across the country, São Paulo has the highest rate among all Brazilian states. METHODS: To identify the highest-risk municipalities during the initial outbreak, we utilized daily confirmed case data from official reports between February 25 and May 5, 2020, which were aggregated to the municipality level. A prospective space-time scan statistic was conducted to detect active clusters in three different time periods. RESULTS: Our findings suggest that approximately 4.6 times more municipalities belong to a significant space-time cluster with a relative risk (RR) > 1 on May 5, 2020. CONCLUSIONS: Our study demonstrated the applicability of the space-time scan statistic for the detection of emerging clusters of COVID-19. In particular, we identified the clusters and RR of municipalities in the initial months of the pandemic, explaining the spatiotemporal patterns of COVID-19 transmission in the state of São Paulo. These results can be used to improve disease monitoring and facilitate targeted interventions.

Mapping the Pandemic

La cartographie de la prévalence et de la propagation des maladies infectieuses n’a jamais été plus cruciale que dans le contexte de la pandémie de COVID-19. Une pléthore de tableaux de bord de SIG en ligne incorporant la fonctionnalité SIG de base ont été créés ;ces tableaux de bord ont servi de plateforme pour le partage rapide de données et la communication d’information en temps réel, facilitant somme toute la prise de décisions. Toutefois, bon nombre de ces tableaux ont été axés uniquement sur la présentation et le contrôle de l’incidence cumulative ou quotidienne des données sur la COVID-19, sans égard à la dimension temporelle. Les auteurs se penchent sur l’utilité des tableaux de bord basés sur les SIG pour cartographier la prévalence de la COVID-19, mais également sur les occasions manquées de mettre l’accent sur le composant temporel de la maladie (cyclicité, saisonnalité). Ils évoquent la possibilité d’un recours aux techniques avancées de géovisualisation pour intégrer le composant temporel aux cartes animées interactives illustrant a) le risque relatif quotidien et le nombre de jours pendant lesquels une zone géographique a été un foyer de contagion, b) le ratio du nombre de cas observés par rapport au nombre de cas prévus dans le temps et c) la dynamique du nombre des décès dans un cube espace-temps. Les auteurs illustrent ces méthodes au moyen des cas de COVID-19 et du nombre des décès aux États-Unis, à l’échelon des comtés, entre le 25 janvier et le 1er octobre 2020. Ils expliquent comment chacune de ces méthodes de visualisation peut faciliter la compréhension d’importants concepts de santé publique appliqués à la pandémie comme le risque, la propagation et le taux de mortalité. Enfin, les auteurs proposent des pistes à envisager pour promouvoir la recherche au carrefour de la visualisation spatiotemporelle et des maladies infectieuses. Mapping the prevalence and spread of infectious diseases has never been more critical than during the COVID-19 pandemic. A plethora of Web-based GIS dashboards have been created that incorporate basic GIS functionality;these dashboards have served as platforms for rapid data sharing and real-time information, ultimately facilitating decision making. However, many of them have merely focused on presenting and monitoring cumulative or daily incidence of COVID-19 data, disregarding the temporal dimension. In this paper, we review the usefulness of GIS-based dashboards for mapping the prevalence of COVID-19, but also missed opportunities to emphasize the temporal component of the disease (cyclicity, seasonality). We suggest that advanced geovisualization techniques can be used to integrate the temporal component in interactive animated maps illustrating (a) the daily relative risk and the number of days a geographic region has been in a disease cluster, (b) the ratio between the observed and expected number of cases over time, and (c) mortality count dynamics in a space–time cube. We illustrate these approaches by using COVID-19 cases and death counts across the U.S. at the county level from 25 January 2020 to 1 October 2020. We discuss how each of these visualization approaches can promote the understanding of important public health concepts applied to the pandemic such as risk, spread, and mortality. Finally, we suggest future avenues to promote research at the intersection of space–time visualization and infectious diseases.

Daily surveillance of COVID-19 using the prospective space-time scan statistic in the United States.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first discovered in late 2019 in Wuhan City, China. The virus may cause novel coronavirus disease 2019 (COVID-19) in symptomatic individuals. Since December of 2019, there have been over 7,000,000 confirmed cases and over 400,000 confirmed deaths worldwide. In the United States (U.S.), there have been over 2,000,000 confirmed cases and over 110,000 confirmed deaths. COVID-19 case data in the United States has been updated daily at the county level since the first case was reported in January of 2020. There currently lacks a study that showcases the novelty of daily COVID-19 surveillance using space-time cluster detection techniques. In this paper, we utilize a prospective Poisson space-time scan statistic to detect daily clusters of COVID-19 at the county level in the contiguous 48 U.S. and Washington D.C. As the pandemic progresses, we generally find an increase of smaller clusters of remarkably steady relative risk. Daily tracking of significant space-time clusters can facilitate decision-making and public health resource allocation by evaluating and visualizing the size, relative risk, and locations that are identified as COVID-19 hotspots.