Detecting space-time clusters of COVID-19 in Brazil: mortality, inequality, socioeconomic vulnerability, and the relative risk of the disease in Brazilian municipalities.

The first case of COVID-19 in South America occurred in Brazil on February 25, 2020. By July 20, 2020, there were 2,118,646 confirmed cases and 80,120 confirmed deaths. To assist with the development of preventive measures and targeted interventions to combat the pandemic in Brazil, we present a geographic study to detect "active" and "emerging" space-time clusters of COVID-19. We document the relationship between relative risk of COVID-19 and mortality, inequality, socioeconomic vulnerability variables. We used the prospective space-time scan statistic to detect daily COVID-19 clusters and examine the relative risk between February 25-June 7, 2020, and February 25-July 20, 2020, in 5570 Brazilian municipalities. We apply a Generalized Linear Model (GLM) to assess whether mortality rate, GINI index, and social inequality are predictors for the relative risk of each cluster. We detected 7 "active" clusters in the first time period, being one in the north, two in the northeast, two in the southeast, one in the south, and one in the capital of Brazil. In the second period, we found 9 clusters with RR > 1 located in all Brazilian regions. The results obtained through the GLM showed that there is a significant positive correlation between the predictor variables in relation to the relative risk of COVID-19. Given the presence of spatial autocorrelation in the GLM residuals, a spatial lag model was conducted that revealed that spatial effects, and both GINI index and mortality rate were strong predictors in the increase in COVID-19 relative risk in Brazil. Our research can be utilized to improve COVID-19 response and planning in all Brazilian states. The results from this study are particularly salient to public health, as they can guide targeted intervention measures, lowering the magnitude and spread of COVID-19. They can also improve resource allocation such as tests and vaccines (when available) by informing key public health officials about the highest risk areas of COVID-19.

Knowledge, attitudes, and practices regarding dengue, chikungunya, and Zika in Cali, Colombia.

Dengue fever (DENF), chikungunya (CHIK), and Zika are responsible for the majority of the burden caused by vector-borne diseases (VBDs); which are produced by viruses primarily transmitted by the Aedes mosquito. Aedes have become prolific in urban areas due to a combination of climate change, rapid urbanization, increased human mobility, and globalization, causing the three VBDs to emerge in novel regions. Community knowledge can provide detailed insights about the spatial heterogeneity of disease risk and rates within a particular region, improving public health interventions. Knowledge, Attitude, and Practice (KAP) surveys are used to shed light on at-risk communities' understanding of the vector, the pathogen, prevention and treatment strategies. Little is known how KAP varies among diseases, and among neighborhoods within a city. Understanding KAP variation among co-circulating VBDs at a fine-level, especially differences between endemic and emerging diseases, can improve targeted interventions, education programs, and health policy. We administered KAP surveys to 327 individuals in healthcare centers and selected neighborhoods in Cali, Colombia in June 2019. We utilized generalized linear models (GLMs) to identify significant predictors of KAP. Our findings suggest that knowledge is related to community characteristics (e.g. strata), while attitudes and practices are more related to individual-level factors. Access to healthcare also forms significant predictor of residents participating in preventative practices. The results can be leveraged to inform public health officials and communities to motivate at-risk neighborhoods to take an active role in vector surveillance and control, while improving educational and surveillance resources in Cali, Colombia.

Rapid surveillance of COVID-19 in the United States using a prospective space-time scan statistic: Detecting and evaluating emerging clusters.

Coronavirus disease 2019 (COVID-19) was first identified in Wuhan, China in December 2019, and is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 is a pandemic with an estimated death rate between 1% and 5%; and an estimated R 0 between 2.2 and 6.7 according to various sources. As of March 28th, 2020, there were over 649,000 confirmed cases and 30,249 total deaths, globally. In the United States, there were over 115,500 cases and 1891 deaths and this number is likely to increase rapidly. It is critical to detect clusters of COVID-19 to better allocate resources and improve decision-making as the outbreaks continue to grow. Using daily case data at the county level provided by Johns Hopkins University, we conducted a prospective spatial-temporal analysis with SaTScan. We detect statistically significant space-time clusters of COVID-19 at the county level in the U.S. between January 22nd-March 9th, 2020, and January 22nd-March 27th, 2020. The space-time prospective scan statistic detected "active" and emerging clusters that are present at the end of our study periods - notably, 18 more clusters were detected when adding the updated case data. These timely results can inform public health officials and decision makers about where to improve the allocation of resources, testing sites; also, where to implement stricter quarantines and travel bans. As more data becomes available, the statistic can be rerun to support timely surveillance of COVID-19, demonstrated here. Our research is the first geographic study that utilizes space-time statistics to monitor COVID-19 in the U.S.

Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016.

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Putting the "landscape" in landscape genetics.

Landscape genetics has emerged as a new research area that integrates population genetics, landscape ecology and spatial statistics. Researchers in this field can combine the high resolution of genetic markers with spatial data and a variety of statistical methods to evaluate the role that landscape variables play in shaping genetic diversity and population structure. While interest in this research area is growing rapidly, our ability to fully utilize landscape data, test explicit hypotheses and truly integrate these diverse disciplines has lagged behind. Part of the current challenge in the development of the field of landscape genetics is bridging the communication and knowledge gap between these highly specific and technical disciplines. The goal of this review is to help bridge this gap by exposing geneticists to terminology, sampling methods and analysis techniques widely used in landscape ecology and spatial statistics but rarely addressed in the genetics literature. We offer a definition for the term "landscape genetics", provide an overview of the landscape genetics literature, give guidelines for appropriate sampling design and useful analysis techniques, and discuss future directions in the field. We hope, this review will stimulate increased dialog and enhance interdisciplinary collaborations advancing this exciting new field.