Covid-19 Space-time Cluster Detection Using Retrospective Analysis

Background: As of the 31st of January 2021, there had been 102,399,513 confirmed cases of COVID-19 worldwide, with 2,217,005 deaths reported to WHOThe goal of this study is to uncover the spatiotemporal patterns of COVID 19 in Ethiopia, which will aid in the planning and implementation of essential preventative measures. Methods We obtained data on COVID 19 cases reported in Ethiopia from November 23 to December 29, 2021, from an Ethiopian health data website that is open to the public.Kulldorff's retrospective space-time scan statistics were utilized to detect the temporal, geographical, and spatiotemporal clusters of COVID 19 at the county level in Ethiopia, using the discrete Poisson probability model. Results: In Ethiopia, between November 23 and December 29, 2021, a total of 22,199 COVID 19 cases were reported.The COVID 19 cases in Ethiopia were strongly clustered in spatial, temporal, and spatiotemporal distribution, according to the results of Kulldorff's scan. statisticsThe most likely Spatio-temporal cluster (LLR = 70369.783209, RR = 412.48, P 0.001) was mostly concentrated in Addis Ababa and clustered between 2021/11/1 and 2021/11/30.Conclusion: From November 23 to December 29, 2021, this study found three large COVID 19 space-time clusters in Ethiopia, which could aid in future resource allocation in high-risk locations for COVID 19 management and prevention.

Spatiotemporal patterns and progression of the Delta variant of COVID-19 and their health intervention linkages in Southeast Asia

The global pandemic of COVID-19 presented an unprecedented challenge to all countries in the world, among which Southeast Asia (SEA) countries managed to maintain and mitigate the first wave of COVID-19 in 2020. However, these countries were caught in the crisis after the Delta variant was introduced to SEA, though many countries had immediately implemented non-pharmaceutical intervention (NPI) measures along with vaccination in order to contain the disease spread. To investigate the potential linkages between epidemic dynamics and public health interventions, we adopted a prospective space-time scan method to conduct spatiotemporal analysis at the district level in the seven selected countries in SEA from June 2021 to October 2021. Results reveal the spatial and temporal propagation and progression of COVID-19 risks relative to public health measures implemented by different countries. Our research benefits continuous improvements of public health strategies in preventing and containing this pandemic.

Sociodemographic and spatiotemporal profiles of hepatitis-A in the state of Pará, Brazil, based on reported notified cases

Hepatitis-A virus is a worldwide healthcare problem, mainly affecting countries with poor sanitary and socioeconomic conditions. This communication evaluates the spatiotemporal variability of the disease’s socioepidemiological profile in one of the endemic Brazilian regions (Pará State) prior to (2008-2013) and after (2014-2017) the launch of the national public vaccination programme. Hepatitis-A epidemiological reports concerning Pará State - Brazil - were used for this study including municipalitylevel data of the disease’s reported positive notification cases (PNCs). The analyses involved socioepidemiological profiling and space-time scan statistics. A total of 5500 PNCs were reported in the study period. On average, PNCs decreased over time throughout the state, with strongest drops after 2015. The PNCs were specific for gender, race/ethnic origin and age group. The predominant gender and race/ethnic groups was male and brown, respectively. While children were the most susceptible age group prior to 2015, there was a shift towards older ages (young and adults) in later years. Those found to be the most affected by the disease, as shown by space-time scan statistics, were people in densely populated municipalities with unsatisfactory sanitary conditions and also less well covered by the public vaccination programme. Despite drops in the number of hepatitis-A PNCs, thanks to the national vaccination programme, the disease still persists in Pará State and elsewhere in Brazil. The present study reinforces the need of continuous prevention and control strategies for effective control and erradication of hepatitis-A.

Spatiotemporal analyses illuminate the competitive advantage of a SARS-CoV-2 variant of concern over a variant of interest

The emergence of novel SARS-CoV-2 variants in late 2020 and early 2021 raised alarm worldwide and prompted reassessment of the management, surveillance, and projected future of COVID-19. Mutations that confer competitive advantages by increasing transmissibility or immune evasion have been associated with the localized dominance of single variants. Thus, elucidating the evolutionary and epidemiological dynamics among novel variants is essential for understanding the trajectory of the COVID-19 pandemic. Here we show the interplay between B.1.1.7 (Alpha) and B.1.526 (Iota) in New York (NY) from December 2020 to April 2021 through phylogeographic analyses, space-time scan statistics, and cartographic visualization. Our results indicate that B.1.526 likely evolved in the Bronx in late 2020, providing opportunity for an initial foothold in the heavily interconnected New York City (NYC) region, as evidenced by numerous exportations to surrounding locations. In contrast, B.1.1.7 became dominant in regions of upstate NY where B.1.526 had limited presence, suggesting that B.1.1.7 was able to spread more efficiently in the absence of B.1.526. Clusters discovered from the spatial-time scan analysis supported the role of competition between B.1.526 and B.1.1.7 in NYC in March 2021 and the outsized presence of B.1.1.7 in upstate NY in April 2021. Although B.1.526 likely delayed the rise of B.1.1.7 in NYC, B.1.1.7 became the dominant variant in the Metro region by the end of the study period. These results reveal the advantages endemicity may grant to a variant (founder effect), despite the higher fitness of an introduced lineage. Our research highlights the dynamics of inter-variant competition at a time when B.1.617.2 (Delta) is overtaking B.1.1.7 as the dominant lineage worldwide. We believe our combined spatiotemporal methodologies can disentangle the complexities of shifting SARS-CoV-2 variant landscapes at a time when the evolution of variants with additional fitness advantages is impending.

Spatiotemporal Characteristics and Risk Factors of the COVID-19 Pandemic in New York State: Implication of Future Policies

The Coronavirus disease 2019 (COVID-19) has been spreading in New York State since March 2020, posing health and socioeconomic threats to many areas. Statistics of daily confirmed cases and deaths in New York State have been growing and declining amid changing policies and environmental factors. Based on the county-level COVID-19 cases and environmental factors in the state from March to December 2020, this study investigates spatiotemporal clustering patterns using spatial autocorrelation and space-time scan analysis. Environmental factors influencing the COVID-19 spread were analyzed based on the Geodetector model. Infection clusters first appeared in southern New York State and then moved to the central western parts as the epidemic developed. The statistical results of space-time scan analysis are consistent with those of spatial autocorrelation analysis. The analysis results of Geodetector showed that both temperature and population density were strong indications of the monthly incidence of COVID-19, especially in March and April 2020. There is a trend of increasing interactions between various risk factors. This study explores the spatiotemporal pattern of COVID-19 in New York State over ten months and explains the relationship between the disease transmission and influencing factors.

Study the Effects of COVID-19 in Punjab, Pakistan using Space-time Scan Statistic for Policy Measures in Regional Agriculture and Food Supply Chain

Food service and retailing sectors play a vital role in economics of Punjab, Pakistan. Pakistan is included in top 50 countries which are estimated to face serious agriculture and food deficiency related challenges due to the world-wide pandemic coronavirus 2019 (COVID-19). The aim of this study was to study the effects of COVID-19 on food security and agriculture in Punjab, Pakistan using space-time scan statistic (STSS). A survey was conducted at 720 points in different districts of the province. The STSS detected “active” and emerging clusters that are current at the end of our study aera – particularly 17 clusters were formed while adding the updated case data. ArcGIS 10.3 software was used to find relative risk (RR) values; the maximum RR value was found to be 42.19 and maximum observed cases 53265 during June 15th – July 1st. Due to the highest number of cases of COVID-19 and RR vales during July, mostly farmers faced many difficulties during the cultivation of cotton and rice. Mostly farmers (72%) observed increase in prices of inputs (fertilizers and pesticides) during lockdown. The timely results (attained through STSS and RR) can inform decision makers and public health officials about where to improve the allocation of resources (including those for farming community), also, where to apply stricter quarantines and travel bans. If the supply chain of agriculture related inputs is disturbed, farmers may find it quite difficult to access markets, which could result in a decline in production and sales of crops and livestock in study area. It is suggested that to protection of food security and to decrease the effect of the lockdown, Punjab government needs to review food policy as well as analyze how market forces will respond to the imbalanced storage facilities and capacity, supply and demand, and price control of products. The findings of this study can also help policy-makers to formulate an effective food security and agriculture adaptation strategy.