Due to spatial and temporal changes in climate, the incidences of COVID-19 is much more higher in some parts of America, Europe and Asia by comparing with Saharan and sub-Saharan Africa. Several studies show the link between climate factors (e.g., temperature rainfall and humidity) and COVID-19 occurrence will be used to aid intervention planning, prevention and control policies. Nigeria is a country that is sensitive to spatial and temporal variability in the occurrence of climate factors, and fully knowing it link with COVID-19 is crucial towards mitigation. In this study, we examined the link by firstly deployed convenience sampling to select three cities (Abuja, Kano and Lagos) where the international airports of Nigeria are situated and also the index case of the country came through Lagos. Secondly, we used the reported cases of COVID-19 from its onset in the country (22/02/2020) up to 21/05/2021. Thirdly, lagged regression was used to explore the link between weekly counts of COVID-19 cases and weekly recorded average of the climate data; including the google trend index as a measure of the populace health seeking behaviour. We found a significant influence of temperature, humidity and heath seeking trend, with a very negligible contributions of precipitation to the occurrence of the COVID-19 in the states investigated. This result will assist policy makers with a prior knowledge to plan for non-pharmaceutical interventions in anticipation of possible outbreak.
Published in | International Journal of Data Science and Analysis (Volume 8, Issue 2) |
DOI | 10.11648/j.ijdsa.20220802.12 |
Page(s) | 23-29 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2022. Published by Science Publishing Group |
COVID-19, Climate, Lagged, Regression, Trend
[1] | Melnick, E. R., & Ioannidis, J. P. (2020). Should governments continue lockdown to slow the spread of COVID-19. BMJ, 369. |
[2] | Costa Dias, M., Joyce, R., Postel-Vinay, F., & Xu, X. (2020). The challenges for labour market policy during the COVID-19 pandemic. Fiscal Studies, 41 (2), 371-382. |
[3] | Alam, M. S., & Sultana, R. (2021). Influences of Climatic and non-climatic factors on COVID-19 outbreak: a review of existing literature. Environmental Challenges, 100255. |
[4] | Zhu, H., Wei, L., & Niu, P. (2020). The novel coronavirus outbreak in Wuhan, China. Global health research and policy, 5 (1), 1-3. |
[5] | Ciaffi, J., Meliconi, R., Landini, M. P., & Ursini, F. (2020). Google trends and COVID-19 in Italy: could we brace for impact. Internal and Emergency Medicine, 15, 1555-1559. |
[6] | Mavragani, A., & Gkillas, K. (2020). COVID-19 predictability in the United States using Google Trends time series. Scientific reports, 10 (1), 1-12. |
[7] | Effenberger, M., Kronbichler, A., Shin, J. I., Mayer, G., Tilg, H., & Perco, P. (2020). Association of the COVID-19 pandemic with internet search volumes: a Google TrendsTM analysis. International Journal of Infectious Diseases, 95, 192-197. |
[8] | Kucharski, A. J., Russell, T. W., Diamond, C., Liu, Y., Edmunds, J., Funk, S., & Flasche, S. (2020). Early dynamics of transmission and control of COVID-19: a mathematical modelling study. The lancet infectious diseases, 20 (5), 553-558. |
[9] | Li, R., Pei, S., Chen, B., Song, Y., Zhang, T., Yang, W., & Shaman, J. (2020). Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV2). Science, 368 (6490), 489-493. |
[10] | Zhao, J., Yuan, Q., Wang, H., Liu, W., Liao, X., Su, Y., & Zhang, Z. (2020). Antibody responses to SARS-CoV-2 in patients with novel coronavirus disease 2019. Clinical infectious diseases, 71 (16), 2027-2034. |
[11] | Jombart, T., Van Zandvoort, K., Russell, T. W., Jarvis, C. I., Gimma, A., Abbott, S., & Bosse, N. I. (2020). Inferring the number of COVID-19 cases from recently reported deaths. Wellcome Open Research, 5. |
[12] | Flaxman, S., Mishra, S., Gandy, A., Unwin, H. J. T., Mellan, T. A., Coupland, H., & Bhatt, S. (2020). Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe. Nature, 584 (7820), 257-261. |
[13] | Hellewell, J., Abbott, S., Gimma, A., Bosse, N. I., Jarvis, C. I., Russell, T. W., & Eggo, R. M. (2020). Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts. The Lancet Global Health, 8 (4), e488-e496. |
[14] | Ferguson, N. M., Laydon, D., Nedjati-Gilani, G., Imai, N., Ainslie, K., Baguelin, M., & Hinsley, W. (2020). Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand. Imperial College COVID-19 Response Team. Imperial College COVID-19 Response Team, 20. |
[15] | Blakely, T., Thompson, J., Bablani, L., Andersen, P., Ouakrim, D. A., Carvalho, N., & Stevenson, M. (2021). Determining the optimal COVID-19 policy response using agentbased modelling linked to health and cost modelling: Case study for Victoria, Australia. Medrxiv. |
[16] | Jun, S. P., Yoo, H. S., & Choi, S. (2018). Ten years of research change using Google Trends: From the perspective of big data utilizations and applications. Technological forecasting and social change, 130, 69-87. |
[17] | Akay, A., & Hess, H. (2019). Deep learning: current and emerging applications in medicine and technology. IEEE journal of biomedical and health informatics, 23 (3), 906-920. |
[18] | Lu, F. S., Hou, S., Baltrusaitis, K., Shah, M., Leskovec, J., Hawkins, J., & Santillana, M. (2018). Accurate influenza monitoring and forecasting using novel internet data streams: a case study in the Boston Metropolis. JMIR public health and surveillance, 4 (1), e4. |
[19] | Husnayain, A., Fuad, A., & Su, E. C. Y. (2020). Applications of Google Search Trends for risk communication in infectious disease management: A case study of the COVID-19 outbreak in Taiwan. International Journal of Infectious Diseases, 95, 221-223. |
[20] | Alzahrani, S. I., Aljamaan, I. A., & Al-Fakih, E. A. (2020). Forecasting the spread of the COVID-19 pandemic in Saudi Arabia using ARIMA prediction model under current public health interventions. Journal of infection and public health, 13 (7), 914-919. |
[21] | Ogundokun, R. O., Lukman, A. F., Kibria, G. B., Awotunde, J. B., & Aladeitan, B. B. (2020). Predictive modelling of COVID-19 confirmed cases in Nigeria. Infectious Disease Modelling, 5, 543-548. |
[22] | Du, Z., Xu, L., Zhang, W., Zhang, D., Yu, S., & Hao, Y. (2017). Predicting the hand, foot, and mouth disease incidence using search engine query data and climate variables: an ecological study in Guangdong, China. BMJ open, 7 (10), e016263. |
[23] | Zhao, D., Wang, L., Cheng, J., Xu, J., Xu, Z., Xie, M., & Su, H. (2017). Impact of weather factors on hand, foot and mouth disease, and its role in short-term incidence trend forecast in Huainan City, Anhui Province. International journal of biometeorology, 61 (3), 453-461. |
[24] | Brandenburg, A. (2020). Piecewise quadratic growth during the 2019 novel coronavirus epidemic. Infectious Disease Modelling, 5, 681-690. |
[25] | Rath, S., Tripathy, A., & Tripathy, A. R. (2020). Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14 (5), 1467-1474. |
[26] | Forstmeier, W., & Schielzeth, H. (2011). Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner’s curse. Behavioral ecology and sociobiology, 65 (1), 47-55. |
[27] | Senapati, A., Maji, S., & Mondal, A. (2021). Piece-wise linear regression: A new approach to predict COVID-19 spreading. In IOP Conference Series: Materials Science and Engineering (Vol. 1020, No. 1, p. 012017). IOP Publishing. |
[28] | Qi, H., Xiao, S., Shi, R., Ward, M. P., Chen, Y., Tu, W., & Zhang, Z. (2020). COVID-19 transmission in Mainland China is associated with temperature and humidity: a time-series analysis. Science of the total environment, 728, 138778. |
[29] | Anttiroiko, A. V. (2021). Successful government responses to the pandemic: Contextualizing national and urban responses to the COVID-19 outbreak in east and west. International Journal of E-Planning Research (IJEPR), 10 (2), 1-17. |
[30] | Ohia, C., Bakarey, A. S., & Ahmad, T. (2020). COVID-19 and Nigeria: putting the realities in context. International Journal of Infectious Diseases, 95, 279-281. |
[31] | Taiwo, J. N., & Falohun, T. O. (2016). SMEs financing and its effects on Nigerian economic growth. European Journal of Business, Economics and Accountancy, 4 (4). |
[32] | Modu, B., Asyhari, A. T., & Peng, Y. (2016, December). Data Analytics of climatic factor influence on the impact of malaria incidence. In 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1-8). IEEE. |
[33] | Osikomaiya, B., Erinoso, O., Wright, K. O., Odusola, A. O., Thomas, B., Adeyemi, O., & Abayomi, A. (2021). ‘Long COVID’: persistent COVID-19 symptoms in survivors managed in Lagos State, Nigeria. BMC Infectious Diseases, 21 (1), 1-7. |
APA Style
Audu Musa Mabu, Babagana Modu, Babagana Ibrahim Bukar, Musa Ibrahim Dagona. (2022). Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria. International Journal of Data Science and Analysis, 8(2), 23-29. https://doi.org/10.11648/j.ijdsa.20220802.12
ACS Style
Audu Musa Mabu; Babagana Modu; Babagana Ibrahim Bukar; Musa Ibrahim Dagona. Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria. Int. J. Data Sci. Anal. 2022, 8(2), 23-29. doi: 10.11648/j.ijdsa.20220802.12
AMA Style
Audu Musa Mabu, Babagana Modu, Babagana Ibrahim Bukar, Musa Ibrahim Dagona. Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria. Int J Data Sci Anal. 2022;8(2):23-29. doi: 10.11648/j.ijdsa.20220802.12
@article{10.11648/j.ijdsa.20220802.12, author = {Audu Musa Mabu and Babagana Modu and Babagana Ibrahim Bukar and Musa Ibrahim Dagona}, title = {Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria}, journal = {International Journal of Data Science and Analysis}, volume = {8}, number = {2}, pages = {23-29}, doi = {10.11648/j.ijdsa.20220802.12}, url = {https://doi.org/10.11648/j.ijdsa.20220802.12}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20220802.12}, abstract = {Due to spatial and temporal changes in climate, the incidences of COVID-19 is much more higher in some parts of America, Europe and Asia by comparing with Saharan and sub-Saharan Africa. Several studies show the link between climate factors (e.g., temperature rainfall and humidity) and COVID-19 occurrence will be used to aid intervention planning, prevention and control policies. Nigeria is a country that is sensitive to spatial and temporal variability in the occurrence of climate factors, and fully knowing it link with COVID-19 is crucial towards mitigation. In this study, we examined the link by firstly deployed convenience sampling to select three cities (Abuja, Kano and Lagos) where the international airports of Nigeria are situated and also the index case of the country came through Lagos. Secondly, we used the reported cases of COVID-19 from its onset in the country (22/02/2020) up to 21/05/2021. Thirdly, lagged regression was used to explore the link between weekly counts of COVID-19 cases and weekly recorded average of the climate data; including the google trend index as a measure of the populace health seeking behaviour. We found a significant influence of temperature, humidity and heath seeking trend, with a very negligible contributions of precipitation to the occurrence of the COVID-19 in the states investigated. This result will assist policy makers with a prior knowledge to plan for non-pharmaceutical interventions in anticipation of possible outbreak.}, year = {2022} }
TY - JOUR T1 - Modeling the Impact of Climate Factors on COVID-19 Transmission in Nigeria AU - Audu Musa Mabu AU - Babagana Modu AU - Babagana Ibrahim Bukar AU - Musa Ibrahim Dagona Y1 - 2022/03/15 PY - 2022 N1 - https://doi.org/10.11648/j.ijdsa.20220802.12 DO - 10.11648/j.ijdsa.20220802.12 T2 - International Journal of Data Science and Analysis JF - International Journal of Data Science and Analysis JO - International Journal of Data Science and Analysis SP - 23 EP - 29 PB - Science Publishing Group SN - 2575-1891 UR - https://doi.org/10.11648/j.ijdsa.20220802.12 AB - Due to spatial and temporal changes in climate, the incidences of COVID-19 is much more higher in some parts of America, Europe and Asia by comparing with Saharan and sub-Saharan Africa. Several studies show the link between climate factors (e.g., temperature rainfall and humidity) and COVID-19 occurrence will be used to aid intervention planning, prevention and control policies. Nigeria is a country that is sensitive to spatial and temporal variability in the occurrence of climate factors, and fully knowing it link with COVID-19 is crucial towards mitigation. In this study, we examined the link by firstly deployed convenience sampling to select three cities (Abuja, Kano and Lagos) where the international airports of Nigeria are situated and also the index case of the country came through Lagos. Secondly, we used the reported cases of COVID-19 from its onset in the country (22/02/2020) up to 21/05/2021. Thirdly, lagged regression was used to explore the link between weekly counts of COVID-19 cases and weekly recorded average of the climate data; including the google trend index as a measure of the populace health seeking behaviour. We found a significant influence of temperature, humidity and heath seeking trend, with a very negligible contributions of precipitation to the occurrence of the COVID-19 in the states investigated. This result will assist policy makers with a prior knowledge to plan for non-pharmaceutical interventions in anticipation of possible outbreak. VL - 8 IS - 2 ER -