Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries
Coronavirus (COVID-19) has continued to be a global threat to public health. As the matter of fact, it needs unreserved effort to monitor the prevalence of the virus. However, applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the s...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2020-01-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S246804272030035X |
| _version_ | 1797205403498446848 |
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| author | Rediat Takele |
| author_facet | Rediat Takele |
| author_sort | Rediat Takele |
| collection | DOAJ |
| description | Coronavirus (COVID-19) has continued to be a global threat to public health. As the matter of fact, it needs unreserved effort to monitor the prevalence of the virus. However, applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the spreading rate. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modeling approach for projecting coronavirus (COVID-19) prevalence patterns in East Africa Countries, mainly Ethiopia, Djibouti, Sudan and Somalia.The data for the study were obtained from the reports of confirmed COVID-19 cases by the official website of Johns Hopkins University from 13th March, 2020 to 30th June, 2020.The results of the study, then, showed that in the coming four month, the number of COVID-19 positive people in Ethiopia may reach up to 56,610 from 5,846 on June 30, 2020 in average-rate scenario. However, in worst case scenario forecast, the model showed that the cases will be around 84,497. The analysis further depicted that with average interventions and control scenario, cumulative number of infected persons in Djibouti, Somalia and Sudan will increase from 4,656, 2,904 and 9,258 respectively at the end of June to 8,336, 3,961 and 21,388, which is by the end of October, 2020, after four-months. But, with insufficient intervention, the number of infected persons may grow quickly and reach up to 14,072, 10,037 and 38,174 in Djibouti, Somalia and Sudan respectively. Generally, the extent of the coronavirus spreading was increased from time to time in the past four month, until 30 th June, 2020, and it is expected to continue quicker than before for the coming 4-month, until the end of October, 2020, in Ethiopia, Djibouti, Somalia, and Sudan and more rapidly than before in Sudan and Ethiopia, while the peak will remain unknown yet. Therefore, an effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may alleviate the spreading rates of the virus, particularly, Sudan and Ethiopia. Moreover, more efforts should be exerted on Ethiopian side to control the population movement across all the border areas and to strengthen border quarantining. Further, through updating more new data with continuous reconsideration of predictive model, provide useful and more precise prediction. Applying, ARIMAX-Transfer Function model in region-wise by take in to consideration of climatic data like temperature and humidity in each countries looking spatial pattern for reliable measure of COVID-19 prevalence. |
| first_indexed | 2024-04-24T08:50:34Z |
| format | Article |
| id | doaj.art-08493bb0761646268f85cee618cbbae2 |
| institution | Directory Open Access Journal |
| issn | 2468-0427 |
| language | English |
| last_indexed | 2024-04-24T08:50:34Z |
| publishDate | 2020-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj.art-08493bb0761646268f85cee618cbbae22024-04-16T11:55:43ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272020-01-015598607Stochastic modelling for predicting COVID-19 prevalence in East Africa CountriesRediat Takele0Assistant Professor in Bio-Statistics, Jigjiga University, Department of Statistics, EthiopiaCoronavirus (COVID-19) has continued to be a global threat to public health. As the matter of fact, it needs unreserved effort to monitor the prevalence of the virus. However, applying an effective prediction of the prevalence is thought to be the fundamental requirement to effectively control the spreading rate. Time series models have extensively been considered as the convenient methods to predict the prevalence or spreading rate of the disease. This study, therefore, aimed to apply the Autoregressive Integrated Moving Average (ARIMA) modeling approach for projecting coronavirus (COVID-19) prevalence patterns in East Africa Countries, mainly Ethiopia, Djibouti, Sudan and Somalia.The data for the study were obtained from the reports of confirmed COVID-19 cases by the official website of Johns Hopkins University from 13th March, 2020 to 30th June, 2020.The results of the study, then, showed that in the coming four month, the number of COVID-19 positive people in Ethiopia may reach up to 56,610 from 5,846 on June 30, 2020 in average-rate scenario. However, in worst case scenario forecast, the model showed that the cases will be around 84,497. The analysis further depicted that with average interventions and control scenario, cumulative number of infected persons in Djibouti, Somalia and Sudan will increase from 4,656, 2,904 and 9,258 respectively at the end of June to 8,336, 3,961 and 21,388, which is by the end of October, 2020, after four-months. But, with insufficient intervention, the number of infected persons may grow quickly and reach up to 14,072, 10,037 and 38,174 in Djibouti, Somalia and Sudan respectively. Generally, the extent of the coronavirus spreading was increased from time to time in the past four month, until 30 th June, 2020, and it is expected to continue quicker than before for the coming 4-month, until the end of October, 2020, in Ethiopia, Djibouti, Somalia, and Sudan and more rapidly than before in Sudan and Ethiopia, while the peak will remain unknown yet. Therefore, an effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may alleviate the spreading rates of the virus, particularly, Sudan and Ethiopia. Moreover, more efforts should be exerted on Ethiopian side to control the population movement across all the border areas and to strengthen border quarantining. Further, through updating more new data with continuous reconsideration of predictive model, provide useful and more precise prediction. Applying, ARIMAX-Transfer Function model in region-wise by take in to consideration of climatic data like temperature and humidity in each countries looking spatial pattern for reliable measure of COVID-19 prevalence.http://www.sciencedirect.com/science/article/pii/S246804272030035XCorona virus caseCOVID19PandemicPredictionPrevalenceARIMA model |
| spellingShingle | Rediat Takele Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries Infectious Disease Modelling Corona virus case COVID19 Pandemic Prediction Prevalence ARIMA model |
| title | Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries |
| title_full | Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries |
| title_fullStr | Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries |
| title_full_unstemmed | Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries |
| title_short | Stochastic modelling for predicting COVID-19 prevalence in East Africa Countries |
| title_sort | stochastic modelling for predicting covid 19 prevalence in east africa countries |
| topic | Corona virus case COVID19 Pandemic Prediction Prevalence ARIMA model |
| url | http://www.sciencedirect.com/science/article/pii/S246804272030035X |
| work_keys_str_mv | AT rediattakele stochasticmodellingforpredictingcovid19prevalenceineastafricacountries |