Flexible scan statistic with a restricted likelihood ratio for optimized COVID-19 surveillance

Submitted: 4 January 2024
Accepted: 9 August 2024
Published: 26 November 2024
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Disease surveillance remains important for early detection of new COVID-19 variants. For this purpose, the World Health Organization (WHO) recommends integrating of COVID-19 surveillance with other respiratory diseases. This requires knowledge of areas with elevated risk, which in developing countries is lacking from the routine analyses. Focusing on Ghana, this study employed scan-statistic cluster analysis to uncover the spatial patterns of incidence and Case Fatality Rates (CFR) of COVID-19 based on reports covering the four pandemic waves in Ghana between 12 March 2020 and 28 February 2022. Applying flexible spatial scan statistic with restricted likelihood ratio, we examined the incidence and CFR clusters before and after adjustment for covariates. We used distance to the epicentre, proportion of the population aged ≥ 65, male proportion of the population and urban proportion of the population as the covariates. We identified 56 significant spatial clusters for incidence and 26 for CFR for all four waves of the pandemic. The Most Likely Clusters (MLCs) of incidence occurred in the districts in south-eastern Ghana, while the CFR ones occurred in districts in the central and the northeastern parts of the country. These districts could serve as sites for sentinel or genomic surveillance. Spatial relationships were identified between COVID-19 incidence covariates and the CFR. We observed closeness to the epicentre and high proportions of urban populations increased COVID-19 incidence, whiles high proportions of those aged ≥ 65 years increased the CFR. Accounting for the covariates resulted in changes in the distribution of the clusters. Both incidence and CFR due to COVID-19 were spatially clustered, and these clusters were affected by high proportions of the urban population, high proportions of the male population, high proportions of the population aged ≥ 65 years and closeness to the epicentre. Surveillance should target districts with elevated risk. Long-term control measures for COVID-19 and other contagious diseases should consider improving quality healthcare access and measures to reduce growth rates of urban populations.

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Abolhassani A, Prates MO, Castellares F, Mahmoodi S, 2020. Zero-inflated bell scan: a more flexible spatial scan statistic. Spat Stat 36:100433.
Adebowale AS, Fagbamigbe AF, Akinyemi JO, Obisesan KO, Awosanya EJ, Afolabi RF, Alarape, SA, Obabiyi, SO, 2021. Situation assessment and natural dynamics of COVID-19 pandemic in Nigeria, 31 May 2020. Scientific African 12: e00844.
Alves HJP, Fernandes FA, de Lima KP, Batista BDO, Fernandes TJ, 2021. Incidence and lethality of COVID-19 clusters in Brazil via circular scan method. Revista Brasileira de Biometria 39:556–70.
Arab-Mazar Z, Sah R, Rabaan AA, Dhama K, Rodriguez-Morales AJ, 2020. Mapping the incidence of the COVID-19 hotspot in Iran – Implications for travellers. Travel Med Infect Dis 34:101630.
Aral N, Bakir H, 2022. Spatiotemporal analysis of COVID 19 in Turkey. Sust Cities Soc 76:0–2.
Bashir M. F, Ma B, Bilal, Komal B, Bashir MA, Tan D, Bashir M, 2020. Correlation between climate indicators and COVID-19 pandemic in New York, USA. Sci Total Environment 728:138835.
Bermudi PMM, Lorenz C, Aguiar BS de, Failla MA, Barrozo LV, Chiaravalloti-Neto F, 2021a. Spatiotemporal ecological study of COVID-19 mortality in the City of São Paulo, Brazil: shifting of the high mortality risk from areas with the best to those with the worst socio-economic conditions. Travel Med Infect Dis 39:101945.
Chen Q, Toorop MMA, De Boer MGJ, Rosendaal FR, Lijfering WM, 2020. Why Crowding Matters in the Time of COVID-19 Pandemic- A Lesson from the Carnival Effect on the 2017/2018 Influenza Epidemic in the Netherlands. BMC Public Health 20:1–10.
European Centre for Disease Prevention and Control, 2021. “Guidance for Representative and Targeted Genomic SARS-CoV-2 Monitoring.” Technical Report 1:1–18. Available from: https://www.who.int/publications/i/item/who-2019-nCoV-surveillanceguidance-2020.8%0Ahttps://www.ecdc.europa.eu/sites/default/files/documents/Guidance-for-representative-and-targeted-genomic-SARS-CoV-2-monitoring.pdf
Fatima M, O’Keefe KJ, Wei W, Arshad S, Gruebner O, 2021. Geospatial Analysis of Covid-19: A Scoping Review. Internat J Environ Res Public Health 18:1–14.
Franch-Pardo I, Napoletano BM, Rosete-Verges F, Billa L, 2020. Spatial Analysis and GIS in the Study of COVID-19. A Review. Sci Total Environ 739:140033.
Ghana Statistical Service, 2021. Ghana 2021 Population and Housing Census General Report3c. Available from: https://statsghana.gov.gh/gssmain/fileUpload/pressrelease/2021%20PHC%20General%20Report%20Vol%203A_Population%20of%20Regions%20and%20Districts_181121.pdf
Ghana Health Service, 2020. Techinical Guidelines for Integrated Disease Surveillance and Response in the WHO Africa Region.Section 4,5,6 and (3rd Edition). Available from: https://ghs.gov.gh/policy-document/
Ghana Health Service, 2022. COVID-19 Situation Dashboard _ Ghana. Ghana Health Service. 2022. Available from: https://www.ghs.gov.gh/covid19/dashboardm.php
Huang Q, Liu Q, Song C, Liu X, Shu H, Wang X, Liu Y, Chen X, Chen J, Pei T, 2021. Urban Spatial Epidemic Simulation Model: A Case Study of the Second COVID-19 Outbreak in Beijing, China. Trans GIS 26:297-316.
Lawton, R., Zheng, K., Zheng, D., & Huang, E. 2021. A longitudinal study of convergence betweenBlack and White COVID-19 mortality: A county fixed effects approach. Lancet Reg Health - Am 1:100011.
Islam A, Sayeed MA, Rahman MK, Ferdous J, Islam S, Hassan MM, 2021. Geospatial Dynamics of COVID-19 Clusters and Hotspots in Bangladesh. Transboundary and Emerging Diseases 68:3643–57.
Jacqui W, 2023. China Coronavirus: WHO Declares International Emergency as Death Toll Exceeds 200. BMJ (Clinical Research Ed.) 368:m408.
Kaburi BB, Kubio C, Kenu E, Ameme DK, Mahama JY, Sackey SO, Afari E A, 2017. Evaluation of Bacterial Meningitis Surveillance Data of the Northern Region, Ghana, 2010-2015. Pan Afr Med J 2017 Jun 30;27:164.
Kulldorff M, 2021. SaTScan User Guide 10.1(Issue July). Available from: https://www.satscan.org/cgi-bin/satscan/register.pl/SaTScan_Users_Guide.pdf?todo=process_userguide_download
Kulldorff M, 1997. Scan Statistic. Encyclopedia of GIS. https://doi.org/10.1007/978-3-319-17885-1_101147.
Otani T, Takahashi, K, 2021. Flexible Scan Statistics for Detecting Spatial Disease Clusters: The Rflexscan r Package. J Statist Software 99 :1–29.
Owusu M, Sylverken AA, Ankrah ST, El-Duah P, Ayisi-Boateng NK, Yeboah R, Gorman R, Asamoah J, Binger T, Acheampong G, Bekoe FA, Ohene SA, Larsen-Reindorf R, Awuah AA, Amuasi J, Owusu-Dabo E, Adu-Sarkodie Y, Phillips RO, 2020. Epidemiological profile of SARS-CoV-2 among selected regions in Ghana: A cross-sectional retrospective study. PLoS One 15(:e0243711.
Paul R, Adeyemi O, Ghosh S, Pokhrel K, Arif AA. 2021. Dynamics of Covid-19 Mortality and Social Determinants of Health: A Spatiotemporal Analysis of Exceedance Probabilities. Ann Epidemiol 62:51–8.
Rodriguez VS, Jacques L, Dalal J, Sestito P, Habibi Z, Venkatasubramanian A, Nguimbis B, Mesa SB, Chimbetete C, Keiser O, Impouma B, Mboussou F, William GS, Ngoy N, Talisuna A, Gueye AS, Hofer CB, Cabore JW, 2021. The Toll of COVID-19 on African Children: A Descriptive Analysis on COVID-19-Related Morbidity and Mortality among the Pediatric Population in Sub-Saharan Africa. Internat J Infect Dis 110:457–65.
US Department of Health and Human, and Centers for Disease Control and Prevention, 2006. Principles of Epidemiology in Public Health Practice, 3rd Edition, no. Cdc: 1–512.
Siljander M, Uusitalo R, Pellikka P, Isosomppi S, Vapalahti O, 2022. Spatiotemporal Clustering Patterns and Sociodemographic Determinants of COVID-19 (SARS-CoV-2) Infections in Helsinki, Finland. Spat Spatio-Temporal Epidemiol 41:100493.
Suleiman AA, Suleiman A, Abdullahi UA, Suleiman SA, 2021. Estimation of the case fatality rate of COVID-19 epidemiological data in Nigeria using statistical regression analysis. Biosafety and Health 3:4–7.
Tango T, Takahashi K, 2012. A flexible spatial scan statistic with a restricted likelihood ratio for detecting disease clusters. Statist Med 31:4207–18.
WHO, 2019. Techinical Guidelines for Integrated Disease Surveillance and Response in the WHO Africa Region.Section 4,5,6 and (3rd Edition). Available from: https://www.afro.who.int/publications/technical-guidelines-integrated-disease-surveillance-and-response-african-region-third
WHO, 2022. Public Health Surveillance for COVID-19. Interim Guidance, no. February: 253–78. Available from: https://www.who.int/publications/i/item/who-2019-nCoV-surveillanceguidance-2020.8.
Yi GY, Hu P, He W, 2020. Characterizing the Dynamic of COVID-19 with a New Epidemic Model: Susceptible-Exposed-Symptomatic-Asymptomatic-Active-Removed. MedRxiv, 2020.12.08.20246264. Available from: https://www.medrxiv.org/content/10.1101/2020.12.08.20246264v1%0Ahttps://www.medrxiv.org/content/10.1101/2020.12.08.20246264v1.abstract.
Zhang J, Dong X, Liu G, Gao Y, 2023. Risk and Protective Factors for COVID-19 Morbidity, Severity, and Mortality. Clin Rev Allergy Immunol 2023;64:90-107.
Zu J, Li ML, Li ZF, Shen MW, Xiao YN, Ji FP, 2020. Transmission Patterns of COVID-19 in the Mainland of China and the Efficacy of Different Control Strategies: A Data- And Model-Driven Study. Infect Dis Poverty 9:1–14.

How to Cite

Akyereko, E., Osei, F. B., Nyarko, K. M., & Stein, A. (2024). Flexible scan statistic with a restricted likelihood ratio for optimized COVID-19 surveillance. Geospatial Health, 19(2). https://doi.org/10.4081/gh.2024.1265