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Spatial-temporal trends of COVID-19 infection and mortality in Sudan.

ABSTRACT: Since its emergence, the coronavirus disease 2019 (COVID-19), is constantly affecting many parts of the globe and threatening millions of lives worldwide. Charting and aligning disease incidence to identify spatial clustering and patterns continue to be a substantial pathway to understanding disease epidemiology and is essential for implementing effective planning and prevention strategies. A national descriptive study was implemented to present the infection and mortality rates of the COVID-19 pandemic in all states of Sudan. Data were collected and summarized in monthly statistical reports of COVID-19 infection and mortality rates. The reports used were from May 2020 to March 2021. The highest COVID-19 incidence rate occurred in December 2020 with a total incidence of 4863 cases ranging from 0 cases in some of the states to 4164 cases in other states (mean = 270 ± 946, median = 21 cases). Followed by the incidence in May 2020 with a total of 4524 cases ranging from 4 to 3509 cases (mean = 251 ± 794, median = 31 cases). The western and southern states of the country had the lowest mortality rates. While, the middle states (Khartoum and El Gezira) had the highest mortalities. Northern and eastern states had lower mortalities than the middle states, yet, higher than the western states. A strong positive correlation between infection and mortality was found.

SUBMITTER: Abd El-Raheem GOH

PROVIDER: S-EPMC9540272 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Spatial-temporal trends of COVID-19 infection and mortality in Sudan.

Abd El-Raheem Ghada Omer Hamad GOH Elamin Hind Eltayeb Salih HES Ahmad Zuhal Mohammednour Omer ZMO Noma Mounkaila M

Scientific reports 20221007 1

Since its emergence, the coronavirus disease 2019 (COVID-19), is constantly affecting many parts of the globe and threatening millions of lives worldwide. Charting and aligning disease incidence to identify spatial clustering and patterns continue to be a substantial pathway to understanding disease epidemiology and is essential for implementing effective planning and prevention strategies. A national descriptive study was implemented to present the infection and mortality rates of the COVID-19 ...[more]

PMID: 36207365

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Project description:Emerging evidence suggests that socially vulnerable communities are at higher risk for coronavirus disease 2019 (COVID-19) outbreaks in the United States. However, no prior studies have examined temporal trends and differential effects of social vulnerability on COVID-19 incidence and death rates. The purpose of this study was to examine temporal trends among counties with high and low social vulnerability and to quantify disparities in these trends over time. We hypothesized that highly vulnerable counties would have higher incidence and death rates compared to less vulnerable counties and that this disparity would widen as the pandemic progressed. We conducted a retrospective longitudinal analysis examining COVID-19 incidence and death rates from March 1 to August 31, 2020 for each county in the US. We obtained daily COVID-19 incident case and death data from USAFacts and the Johns Hopkins Center for Systems Science and Engineering. We classified counties using the Social Vulnerability Index (SVI), a percentile-based measure from the Centers for Disease Control and Prevention in which higher scores represent more vulnerability. Using a Bayesian hierarchical negative binomial model, we estimated daily risk ratios (RRs) comparing counties in the first (lower) and fourth (upper) SVI quartiles. We adjusted for percentage of the county designated as rural, percentage in poor or fair health, percentage of adult smokers, county average daily fine particulate matter (PM2.5), percentage of primary care physicians per 100,000 residents, and the proportion tested for COVID-19 in the state. In unadjusted analyses, we found that for most of March 2020, counties in the upper SVI quartile had significantly fewer cases per 100,000 than lower SVI quartile counties. However, on March 30, we observed a crossover effect in which the RR became significantly greater than 1.00 (RR = 1.10, 95% PI: 1.03, 1.18), indicating that the most vulnerable counties had, on average, higher COVID-19 incidence rates compared to least vulnerable counties. Upper SVI quartile counties had higher death rates on average starting on March 30 (RR = 1.17, 95% PI: 1.01,1.36). The death rate RR achieved a maximum value on July 29 (RR = 3.22, 95% PI: 2.91, 3.58), indicating that most vulnerable counties had, on average, 3.22 times more deaths per million than the least vulnerable counties. However, by late August, the lower quartile started to catch up to the upper quartile. In adjusted models, the RRs were attenuated for both incidence cases and deaths, indicating that the adjustment variables partially explained the associations. We also found positive associations between COVID-19 cases and deaths and percentage of the county designated as rural, percentage of resident in fair or poor health, and average daily PM2.5. Results indicate that the impact of COVID-19 is not static but can migrate from less vulnerable counties to more vulnerable counties over time. This highlights the importance of protecting vulnerable populations as the pandemic unfolds.

Project description:BACKGROUND: Malaria vector control in Sudan relies mainly on indoor residual spraying (IRS) and the use of long lasting insecticide treated bed nets (LLINs). Monitoring insecticide resistance in the main Sudanese malaria vector, Anopheles arabiensis, is essential for planning and implementing an effective vector control program in this country. METHODS: WHO susceptibility tests were used to monitor resistance to insecticides from all four WHO-approved classes of insecticide at four sentinel sites in Gezira state over a three year period. Insecticide resistance mechanisms were studied using PCR and microarray analyses. RESULTS: WHO susceptibility tests showed that Anopheles arabiensis from all sites were fully susceptible to bendiocarb and fenitrothion for the duration of the study (2008-2011). However, resistance to DDT and pyrethroids was detected at three sites, with strong seasonal variations evident at all sites. The 1014 F kdr allele was significantly associated with resistance to pyrethroids and DDT (P?<?0.001) with extremely high effects sizes (OR?>?7 in allelic tests). The 1014S allele was not detected in any of the populations tested. Microarray analysis of the permethrin-resistant population of An. arabiensis from Wad Medani identified a number of metabolic genes that were significantly over-transcribed in the field-collected resistant samples when compared to the susceptible Sudanese An. arabiensis Dongola strain. These included CYP6M2 and CYP6P3, two genes previously implicated in pyrethroid resistance in Anopheles gambiae s.s, and the epsilon-class glutathione-S-transferase, GSTe4. CONCLUSIONS: These data suggest that both target-site mechanisms and metabolic mechanisms play an important role in conferring pyrethroid resistance in An. arabiensis from Sudan. Identification in An. arabiensis of candidate loci that have been implicated in the resistance phenotype in An. gambiae requires further investigation to confirm the role of these genes.

Dataset Information

Spatial-temporal trends of COVID-19 infection and mortality in Sudan.

Publications

Spatial-temporal trends of COVID-19 infection and mortality in Sudan.

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