Exposing Empirical Links between COVID-19 Situation Report and Available Data: The Case of Nigeria
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Design
2.2. Study Context
2.3. Data Sources
2.4. Projected Population
2.5. Procedures for Data Analysis
3. Results and Discussions
3.1. Confirmed COVID-19 Cases per State Population
3.2. COVID-19 Tests per State Population
3.3. Correlation between Test Rate and Confirmed COVID-19 Cases Per State Population
3.4. Correlation between Environmental Conditions and Confirmed COVID-19 Cases per State Population
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Cucinotta, D.; Vanelli, M. WHO declares COVID-19 a pandemic. Acta Biomed. 2020, 91, 157–160. [Google Scholar] [PubMed]
- Africa CDC. Coronavirus Disease 2019 (COVID-19): Latest Updates on the COVID-19 Crisis from Africa CDC. 2020. Available online: https://africacdc.org/covid-19/ (accessed on 8 September 2020).
- Chukwuorji, J.C.; Iorfa, S.K. Commentary on the coronavirus pandemic: Nigeria. Psychol. Trauma Theory Res. Pract. Policy 2020, 12 (Suppl. 1), S188–S190. [Google Scholar] [CrossRef] [PubMed]
- Randolph, H.E.; LBarreiro, B. Herd Immunity: Understanding COVID-19. Immunity 2020, 52, 737–741. [Google Scholar] [CrossRef] [PubMed]
- Osseni, I.A. COVID-19 pandemic in sub-Saharan Africa: Preparedness, response, and hidden potentials. Trop. Med. Health 2020, 48, 48. [Google Scholar] [CrossRef] [PubMed]
- Afolabi, M.O.; Folayan, M.O.; Munung, N.S.; Yakubu, A.; Ndow, G.; Jegede, A.; Ambe, J.; Kombe, F. Lessons from the Ebola epidemics and their applications for COVID-19 pandemic response in sub-Saharan Africa. Dev. World Bioeth. 2020, 1–6. [Google Scholar] [CrossRef] [PubMed]
- Anikwe, C.C.; Ogah, C.O.; Anikwe, I.H.; Okorochukwu, B.C.; Ikeoha, C.C. Coronavirus disease 2019: Knowledge, attitude, and practice of pregnant women in a tertiary hospital in Abakaliki, southeast Nigeria. Int. J. Gynaecol. Obstet. 2020. [Google Scholar] [CrossRef] [PubMed]
- David, K.B.; Adebisi, Y.A. Proposed model for hospital and community pharmacy services during COVID-19 pandemic in Nigeria. Int. J. Pharm. Pract. 2020. [Google Scholar] [CrossRef] [PubMed]
- Zhao, Z.; Li, X.; Liu, F.; Zhu, G.; Ma, C.; Wang, L. Prediction of the COVID-19 spread in African countries and implications for prevention and control: A case study in South Africa, Egypt, Algeria, Nigeria, Senegal and Kenya. Sci. Total Environ. 2020, 729, 138959. [Google Scholar] [CrossRef] [PubMed]
- Okuonghae, D.; Omame, A. Analysis of a mathematical model for COVID-19 population dynamics in Lagos, Nigeria. Chaos Solitons Fractals 2020, 139. [Google Scholar] [CrossRef] [PubMed]
- NCDC. COVID-19 Situation Report: Sittuation Report 158; Nigeria Centre for Disease Control: Abuja, Nigeria, 2020.
- Bryman, A. Social Research Methods; Oxford University Press: Oxford, UK, 2016. [Google Scholar]
- Federal Republic of Nigeria. Nigeria at 50: A Compendium: The Official and Authoritative Book about Nigeria; Federal Republic of Nigeria: Abuja, Nigeria, 2010. [Google Scholar]
- National Bureau of Statistics. Population 2006–2016: National Population Estimates; National Bureau of Statistics: Abuja, Nigeria, 2017.
- NCDC. National Interim Guidelines for Clinical Management of COVID-19; Nigeria Centre for Disease Control: Abuja, Nigeria, 2020; pp. 1–60.
- Godlee, F. Covid-19: Testing testing. Br. Med. J. 2020, 369, m1879. [Google Scholar] [CrossRef]
- Tosepu, R.; Gunawan, J.; Effendy, D.S.; Ahmad, L.A.I.; Lestari, H.; Bahar, H.; Asfian, P. Correlation between weather and Covid-19 pandemic in Jakarta, Indonesia. Sci. Total Environ. 2020, 725. [Google Scholar] [CrossRef] [PubMed]
- Gupta, S.; Raghuwanshi, G.S.; Chanda, A. Effect of weather on COVID-19 spread in the US: A prediction model for India in 2020. Sci. Total Environ. 2020, 728. [Google Scholar] [CrossRef] [PubMed]
- Kim, Y.J.; Sung, H.; Ki, C.-S.; Hur, M. COVID-19 testing in South Korea: Current status and the need for faster diagnostics. Ann. Lab. Med. 2020, 40, 349–350. [Google Scholar] [CrossRef] [PubMed]
- Salathe, M.; Althaus, C.L.; Neher, R.; Stringhini, S.; Hodcroft, E.; Fellay, J.; Zwahlen, M.; Senti, G.; Battegay, M.; Wilder-Smith, A.; et al. COVID-19 epidemic in Switzerland: On the importance of testing, contact tracing and isolation. Swiss Med. Wkly. 2020, 150, w20225. [Google Scholar] [CrossRef] [PubMed]
- Souch, J.M.; Cossman, J.S. A commentary on rural-urban disparities in COVID-19 testing rates per 100,000 and risk factors. J. Rural Health 2020. [Google Scholar] [CrossRef] [PubMed] [Green Version]
State | Population (2006) | Annual Growth Rate | Projected Population (2020) |
---|---|---|---|
Abia | 2,845,380 | 0.027 | 4,152,442 |
Adamawa | 3,178,950 | 0.029 | 4,770,976 |
Akwa Ibom | 3,902,051 | 0.034 | 6,280,831 |
Anambra | 4,177,828 | 0.028 | 6,182,925 |
Bauchi | 4,653,066 | 0.034 | 7,489,682 |
Bayelsa | 1,704,515 | 0.029 | 2,558,140 |
Benue | 4,253,641 | 0.03 | 6,473,878 |
Borno | 4,171,104 | 0.034 | 6,713,905 |
Cross River | 2,892,988 | 0.029 | 4,341,804 |
Delta | 4,112,445 | 0.032 | 6,436,711 |
Ebonyi | 2,176,947 | 0.028 | 3,221,746 |
Edo | 3,233,366 | 0.027 | 4,718,655 |
Ekiti | 2,398,957 | 0.031 | 3,702,595 |
Enugu | 3,267,837 | 0.03 | 4,973,522 |
Gombe | 2,365,040 | 0.032 | 3,701,710 |
Imo | 3,927,563 | 0.032 | 6,147,338 |
Jigawa | 4,361,002 | 0.029 | 6,545,003 |
Kaduna | 6,113,503 | 0.03 | 9,304,517 |
Kano | 9,401,288 | 0.033 | 14,922,150 |
Katsina | 5,801,584 | 0.03 | 8,829,788 |
Kebbi | 3,256,541 | 0.031 | 5,026,207 |
Kogi | 3,314,043 | 0.03 | 5,043,846 |
Kwara | 2,365,353 | 0.03 | 3,599,976 |
Lagos | 9,113,605 | 0.032 | 14,264,420 |
Nasarawa | 1,869,377 | 0.03 | 2,845,120 |
Niger | 3,954,772 | 0.034 | 6,365,692 |
Ogun | 3,751,140 | 0.033 | 5,953,979 |
Ondo | 3,460,877 | 0.03 | 5,267,322 |
Osun | 3,416,959 | 0.032 | 5,348,151 |
Oyo | 5,580,894 | 0.034 | 8,983,135 |
Plateau | 3,206,531 | 0.027 | 4,679,493 |
Rivers | 5,198,716 | 0.034 | 8,367,973 |
Sokoto | 3,702,676 | 0.03 | 5,635,331 |
Taraba | 2,294,800 | 0.029 | 3,444,042 |
Yobe | 2,321,339 | 0.035 | 3,789,159 |
Zamfara | 3,278,873 | 0.032 | 5,132,022 |
FCT | 1,406,239 | 0.093 | 5,170,238 |
Nigeria | 140,431,790 | 0.032 | 220,384,426 |
Mean | Standard Deviation | Number of States Including the FCT | Pearson Correlation Coefficient | p-Value | |
---|---|---|---|---|---|
Infectious rates | 1.669 | 2.173 | 37 | 0.903 * | <0.001 |
Test rates | 11.407 | 14.139 | 37 |
Mean | Standard Deviation | Number of States Including the FCT | Pearson Correlation Coefficient | p-Value(One-Tail) | |
---|---|---|---|---|---|
Infectious rates | 1.669 | 2.173 | 37 | ||
Amount of rainfall (mm) | 1443.000 | 657.740 | 37 | 0.199 | 0.120 |
Temperature (°C) | 26.368 | 1.0047 | 37 | −0.104 | 0.271 |
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Zakariya, Y.F. Exposing Empirical Links between COVID-19 Situation Report and Available Data: The Case of Nigeria. Diseases 2020, 8, 38. https://doi.org/10.3390/diseases8040038
Zakariya YF. Exposing Empirical Links between COVID-19 Situation Report and Available Data: The Case of Nigeria. Diseases. 2020; 8(4):38. https://doi.org/10.3390/diseases8040038
Chicago/Turabian StyleZakariya, Yusuf F. 2020. "Exposing Empirical Links between COVID-19 Situation Report and Available Data: The Case of Nigeria" Diseases 8, no. 4: 38. https://doi.org/10.3390/diseases8040038
APA StyleZakariya, Y. F. (2020). Exposing Empirical Links between COVID-19 Situation Report and Available Data: The Case of Nigeria. Diseases, 8(4), 38. https://doi.org/10.3390/diseases8040038