The goal of this paper is to assess the spread and mortality in the first 120 days of COVID-19 in Nigeria and compare it to seven selected countries in diverse developmental contexts. First, our analysis showed the spread of the pandemic increased following relaxation of lockdowns. Both spread and mortality patterns in Nigeria compared well to other African countries (Ghana, Cameroun and Egypt). Lastly, the findings suggested that different predictive models gave the best fit across countries.
In this study, the COVID-19 data in Nigeria show an increasing trend. The curve for Nigeria is unlike a typical propagated epidemic curve that would have been expected in the case of COVID-19 being an infectious disease. The transmissibility interval of COVID-19, that is its reproductive number which signifies the number of people a single case can infect with the virus, was estimated to be from 1.4 to 2.5, 3.6 to 4.0, and 2.24 to 3.58 by earlier studies [3, 5–7]. An indication that the disease will continue to be on the increase. Therefore, the observed pattern found in our study agrees with the known pattern of spread of the disease. We also found a higher number of cases reported after the relaxation of lockdown than during the lockdown period. This suggests a wider spread of the disease after the relaxation of lockdown, which could be due to poor adherence to the standard precautionary measures in Nigeria. Asides, the increased spread following relaxation of lockdown is not surprising for when restrictive measures are lifted, the rate of exposure to disease risk becomes higher. Consequently, an increased number of infections is likely to follow. This is in tandem with evidence from developed countries where the transmission dynamics and effectiveness of control measures have been rigorously studied [8,9]. However, an increase in capacity for testing may be the possible explanation for the current finding. In comparing with Ghana and South Africa, the testing capacity in Nigeria is generally low. While Nigeria has only 2,755 people tested for COVID-19 per 1,000,000 people, the estimate was 16,206 and 76,067 Ghana and South Africa .
We found a similarity possibly due to about the same capacity for testing in the trajectory pattern of cumulative confirmed cases of COVID-19 in Nigeria, Ghana and Cameroon in the first 120 days after the first outbreak in these countries. Conversely, a difference was observed in the pattern exhibited by Nigeria compared to four of the seven countries investigated (Mexico, Bangladesh, South Africa and Indonesia). As at the 120th day after first case confirmation in these four countries, the total COVID-19 test conducted and test per 1 million population was strikingly higher than Nigeria’s over the same period [1, 10]. Except for Indonesia, which has a comparable population size with Nigeria, the population figure for each of the other seven countries was extraordinarily lower. Due to environmental factors like temperature and humidity [11, 12], one would have expected the pattern of the disease spread in Nigeria to be similar to that of Mexico, Bangladesh, South Africa and Indonesia, all things being equal. Thus, it is tempting to conclude that low testing capacity in Nigeria is responsible for variability in the observed pattern of COVID-19 cases compared to the four countries. The implication is that community testing has not commenced fully in Nigeria as it is done in South Africa; the disease is presently having a sporadic cluster of local transmission in Nigeria.
We further found, within the study period, that the distribution of COVID-19 associated death observed for Nigeria was comparable to 6 of the other 7 countries investigated and aligned perfectly with the Cameroun and Ghana patterns. The prominent difference in COVID-19 related death trajectory found in Mexico compared to other countries may be explained by the higher number of observed COVID-19 cases within the study period. The compactness in the similarity in COVID-19 deaths between Nigeria, Ghana and Cameroon, countries from west Africa may be attributed to other factors aside from the COVID-19 testing capacity and case management capabilities.
The cubic polynomial (CPM) was identified as the model of best fit among the four models used in this study. Next to the CPM is the quadratic model (QM). The CPM and QM have been identified in the previous studies as predictive models for some infectious diseases including Ebola and COVID-19 [13, 14]. None of the data for the countries follows the exponential model except South Africa which aligns with the data within the first 70 days of the outbreak confirmation. A similar remark has been made in the past on the suitability of the exponential model for fitting the spread curve of infectious disease . Nonetheless, differences in the level and mode of testing in each country could be responsible for the South African’s exemption. Aside the case reporting, a community testing approach to identify more cases of COVID-19 was instituted early in South Africa which countries like Nigeria, Ghana and Cameroon did not do within the study period. In addition, one cannot overrule the mark differences in atmospheric and environmental conditions between countries as another pertinent reason [11,12].
In our study, the predicted COVID-19 cumulative case for 30 September 2020 using QM and CPM was 93,988 and 155,467 respectively, provided the present testing capacity for COVID-19 and the level of compliance with the preventive measures to mitigate the disease spread is sustained throughout the period. Of course, the wide gap between the two estimates could be linked to the differences in equations governing the usage of QM and CPM, and as such different parameters used for the estimation. The fact that the best fitting models appear to differ across countries is an indication of variation in the epidemiological context and transmission dynamics and control efforts. Although some of these countries share similarities in demographic and developmental profile, there are differences in factors including testing capacity, risk profile, enforcement of containment measures and exposure to infected individuals.
The public health implication of our study is enormous, there is a need for adequate emergency preparedness. The identified trajectory of COVID-19 infection in Nigeria is an impetus for increased surveillance, enhanced testing capacity and succinct plan for clinical management of cases as well as psychosocial management of discharged cases. Also, the preventive measures may have to be sustained for the spread of the pandemic in Nigeria and other countries to be contained.
The Nigeria data was premised on testing suspected cases who either reported at the testing centres or symptomatic individuals at homes who call the NCDC response team lines for help. The differential in the scope of testing and atmospheric conditions in different countries should not be overruled while interpreting our findings. This is because there is some evidence on the relationship between weather conditions and transmission risks of COVID-19 [11,12]. Inaccessibility to data on socio-demographic profile and health history of the COVID-19 patients and survivors limits the opportunity to do some statistical and mathematical modelling.