COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling

Estimating the Relative Risks of Spatial Clusters Using a Predictor-Corrector Method

Spatial, temporal, and space-time scan statistics can be used for geographical surveillance, identifying temporal and spatial patterns, and detecting outliers. While statistical cluster analysis is a valuable tool for identifying patterns, optimizing resource allocation, and supporting decision-making, accurately predicting future spatial clusters remains a significant challenge. Given the known relative risks of spatial clusters over the past k time intervals, the main objective of the present study is to predict the relative risks for the subsequent interval, k + 1 . Building on our prior research, we propose a predictive Markov chain model with an embedded corrector component. This corrector utilizes either multiple linear regression or exponential smoothing method, selecting the one that minimizes the relative distance between observed and predicted values in the k -th interval. To test the proposed method, we first calculated the relative risks of statistically significant spatial clusters of COVID-19 mortality in the U.S. over seven time intervals from May 2020 to March 2023. Then, for each time interval, we selected the top 25 clusters with the highest relative risks and iteratively predicted the relative risks of clusters from intervals three to seven. The predictive accuracies ranged from moderate to high, indicating the potential applicability of this method for predictive disease analytics and future pandemic preparedness.

Data-driven mathematical modeling approaches for COVID-19: A survey

In this review, we successively present the methods for phenomenological modeling of the evolution of reported and unreported cases of COVID-19, both in the exponential phase of growth and then in a complete epidemic wave. After the case of an isolated wave, we present the modeling of several successive waves separated by endemic stationary periods. Then, we treat the case of multi-compartmental models without or with age structure. Eventually, we review the literature, based on 260 articles selected in 11 sections, ranging from the medical survey of hospital cases to forecasting the dynamics of new cases in the general population. This review favors the phenomenological approach over the mechanistic approach in the choice of references and provides simulations of the evolution of the number of observed cases of COVID-19 for 10 states (California, China, France, India, Israel, Japan, New York, Peru, Spain and United Kingdom).

Spatiotemporal clusters and the socioeconomic determinants of COVID-19 in Toronto neighbourhoods, Canada

The aim of this study is to identify spatiotemporal clusters and the socioeconomic drivers of COVID-19 in Toronto. Geographical, epidemiological, and socioeconomic data from the 140 neighbourhoods in Toronto were used in this study. We used local and global Moran's I, and space-time scan statistic to identify spatial and spatiotemporal clusters of COVID-19. We also used global (spatial regression models), and local geographically weighted regression (GWR) and Multiscale Geographically weighted regression (MGWR) models to identify the globally and locally varying socioeconomic drivers of COVID-19. The global regression model identified a lower percentage of educated people and a higher percentage of immigrants in the neighbourhoods as significant predictors of COVID-19. MGWR shows the best fit model to explain the variables affecting COVID-19. The findings imply that a single intervention package for the entire area would not be an effective strategy for controlling COVID-19; a locally adaptable intervention package would be beneficial.

Understanding the widespread use of veterinary ivermectin for Chagas disease, underlying factors and implications for the COVID-19 pandemic: a convergent mixed-methods study

OBJECTIVES

Veterinary ivermectin (vet-IVM) has been used widely in Latin America against COVID-19, despite the lack of scientific evidence and potential risks. Widespread vet-IVM intake was also discovered against Chagas disease during a study in Bolivia prior to the pandemic. All vet-IVM-related data were extracted to understand this phenomenon, its extent and underlying factors and to discuss potential implications for the current pandemic.

DESIGN

A convergent mixed-methods study design including a survey, qualitative in-depth interviews (IDI) and focus group discussions (FGD).

SETTING

A cross-sectional study conducted in 2018 covering the geographic area of Monteagudo, an endemic municipality for Chagas disease.

PARTICIPANTS

A total of 669 adult household representatives from 26 communities participated in the survey, supplemented by 14 IDI and 2 FGD among patients, relatives and key informants.

RESULTS

9 IDI and 2 FGD contained narratives on vet-IVM use against Chagas disease. Five main themes emerged: (1) the extent of the vet-IVM phenomenon, (2) the perception of vet-IVM as a treatment for Chagas disease, (3) the vet-IVM market and the controversial role of stakeholders, (4) concerns about potential adverse events and (5) underlying factors of vet-IVM use against Chagas disease.In quantitative analysis, 28% of participants seropositive for Chagas disease had taken vet-IVM. Factors associated with multivariate analysis were advanced age (OR 17.01, 95 CI 1.24 to 36.55, p=0.027 for age above 60 years), the experience of someone close as information source (OR 3.13, 95 CI 1.62 to 5.02, p<0.001), seropositivity for Chagas disease (OR 3.89, 95 CI 1.39 to 6.20, p=0.005) and citing the unavailability of benznidazole as perceived healthcare barrier (OR 2.3, 95 CI 1.45 to 5.18, p=0.002). Participants with an academic education were less likely to report vet-IVM intake (OR 0.12, 95 CI 0.01 to 0.78, p=0.029).

CONCLUSIONS

Social determinants of health, the unavailability of treatment and a wonder drug image might contribute to the phenomenon of vet-IVM.

Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review

The spread of the COVID-19 pandemic was spatially heterogeneous around the world; the transmission of the disease is driven by complex spatial and temporal variations in socioenvironmental factors. Spatial tools are useful in supporting COVID-19 control programs. A substantive review of the merits of the methodological approaches used to understand the spatial epidemiology of the disease is hardly undertaken. In this study, we reviewed the methodological approaches used to identify the spatial and spatiotemporal variations of COVID-19 and the socioeconomic, demographic and climatic drivers of such variations. We conducted a systematic literature search of spatial studies of COVID-19 published in English from Embase, Scopus, Medline, and Web of Science databases from 1 January 2019 to 7 September 2021. Methodological quality assessments were also performed using the Joanna Briggs Institute (JBI) risk of bias tool. A total of 154 studies met the inclusion criteria that used frequentist (85%) and Bayesian (15%) modelling approaches to identify spatial clusters and the associated risk factors. Bayesian models in the studies incorporated various spatial, temporal and spatiotemporal effects into the modelling schemes. This review highlighted the need for more local-level advanced Bayesian spatiotemporal modelling through the multi-level framework for COVID-19 prevention and control strategies.

COVID-19 in Brazil: spatial risk, social vulnerability, human development, clinical manifestations and predictors of mortality - a retrospective study with data from 59 695 individuals

Brazil ranks second in the number of confirmed cases of COVID-19 worldwide. In spite of this, coping measures differ throughout the national territory, as does the disease's impact on the population. This cross-sectional observational study, with 59 695 cases of COVID-19 registered in the state of Alagoas between March and August 2020, analysed clinical-epidemiological variables, incidence rate, mortality rate, case fatality rate (CFR) and the social indicators municipal human development index (MHDI) and social vulnerability index (SVI). Moran statistics and regression models were applied. Logistic regression analysis was applied to determine the predictors of death. The incidence rate was 1788.7/100 000 inhabitants; mortality rate was 48.0/100 000 and CFR was 2.7%. The highest incidence rates were observed in municipalities with better human development (overall MHDI (I = 0.1668; p = 0.002), education MHDI (I = 0.1649; p = 0.002) and income MHDI (I = 0.1880; p = 0.005)) and higher social vulnerability (overall SVI (I = 0.0599; p = 0.033)). CFR was associated with higher social vulnerability (SVI human capital (I = 0.0858; p = 0.004) and SVI urban infrastructure (I = 0.0985; p = 0.040)). Of the analysed cases, 55.4% were female; 2/3 were Black or Brown and the median age was 41 years. Among deaths, most were male (919; 57.4%) and elderly (1171; 73.1%). The predictors of death were male sex, advanced age and the presence of comorbidities. In Alagoas, Brazil, the disease has undergone a process of interiorisation and caused more deaths in poorer municipalities. The presence of comorbidities and advanced age were predictors of death.