key: cord-0686088-n1y1mt9s
authors: Likassa, Habte Tadesse; Xain, Wen; Tang, Xuan; Gobebo, Gizachew
title: Predictive models on COVID 19: What Africans should do?
date: 2020-11-17
journal: Infect Dis Model
DOI: 10.1016/j.idm.2020.10.015
sha: ff7f283476710936325bd105f6fade99a70a1f9b
doc_id: 686088
cord_uid: n1y1mt9s

In this study, predictive models are proposed to accurately estimate the confirmed cases and deaths due to of Corona virus 2019 (COVID-19) in Africa. The study proposed the predictive models to determine the spatial and temporal pattern of COVID 19 in Africa. The result of the study has shown that the spatial and temporal pattern of the pandemic is varying across in the study area. The result has shown that cubic model is best outperforming compared to the other six families of exponentials ([Formula: see text]. The adopted cubic algorithm is more robust in predicting the confirmed cases and deaths due to COVID 19. The cubic algorithm is more superior to the state of the art of the works based on the world health organization data. This also entails the best way to mitigate the expansion of COVID 19 is through persistent and strict self-isolation. This pandemic will sustain to grow up, and peak to the highest for which a strong care and public health interventions practically implemented. It is highly recommended for Africans must go beyond theory preparations implementations practically through the public interventions.

Rendering to Nature, a spatial spreading of COVID-19 is attractive overwhelming and has already extended the necessary the standard health benchmarks for the virus to be acknowledged a pandemic, with high infection more than 1.5 million people in 170 countries [16] . On January 7 th , 2020, a potential coronavirus was quarantine and considered as severe acute; respiratory syndrome COVID 19,  where this virus is considered as COVID 19 by the WHO on February 11 th , 2020. The suspections of this novel and potential virus have been regarded and related to the to ABO blood group. For example, Norwalk virus and Hepatitis B have clear blood group susceptibility [1, 2] . The spatial distribution of COVID 19 has ready taken on pandemic rate;

impacting almost over 170 regions in a matter of several weeks a global reaction to prepare health systems to meet this unprecedented challenge. Thereby, a synchronized global reaction is greatly needed to prepare coordinated health systems so as to attain this unparalleled major areas challenge. There are still growing up of expansion countries that have been unsuccessful adequate to have been vulnerable to these diseases already have, illogically, very appreciated lessons to pass this critical time. Though the suppression measures implemented in China have at least for the current moment reduction to the coming new cases by more than 90%, this reducing is not the case in other Europeans and other African countries [17] . Therefore, cogently known the spatial and temporal modeling, prediction rate of its distribution along with its mitigation mechanisms are very important to give some information in the future to mitigate this serious problem. COVID 19 is becoming the world problem for health, depletion of economy and terrorizing the world, unexpected death and hundred thousands of illness. This work is aimed to determine the spatial and temporal distribution, prediction of death rate and mitigation mechanisms of COVID 19: The Case of Africa. Additionally, we also proposed predictive models on COVID 19 in Africa.

In the present paper, we present adopted mathematical models for COVID-19 that incorporates both potential parameters, including both the environment-to-human and human-to-human routes. Temporarily, the diffusion rates in our model depend on the epidemiological status and the confirmed cases and deaths with time. In particular, when the infection level is high, people would be motivated to take necessary action to reduce the contact with the infected individuals and contaminated environment so as to protect themselves and their families, leading to a reduction of the average transmission rates. We adopted two algorithms both are quadratic and curved exponential process to examine the prediction of confirmed and death rates of COVID 19 . There is no a clear indication of the source of the COVID 19, but some recently published works such as [4, 5, 6, 7, 8, 9, 25, 26, 27, 34] indicates as the source of the outbreak belongs to Huanan Seafood whole market. Therefore, the foremost contribution novel ideas of the up-todate are: An efficient and effective prediction models to predict the confirmed number of cases and deaths of the COVID 19. Improved quadratic and curved models are proposed as compared with the other exponential families (logarithmic, logistic, compound, growth and exponential).

Therefore, developing a new robust algorithm is very essential to predict the pandemic.

This study is mainly focusing in Africa where the data are obtained from the daily world health organization.

The nature of data related to this study is purely secondary data obtained from the world health organization. Thereby, all the updated information and data related for this study is obtained from the (World Health Organizations, 2020). The prominent information for the study is based on the confirmed number of cases, number of deaths, and the transmission type, which are conveyed on daily registered by the world health organization. The appropriate data for this study is depending on the world health organization.

J o u r n a l P r e -p r o o f In order to maintain the ultimate goal of this work, we also employed several Evaluations of the Measures such as the cubic, logarithmic, exponential families. The effectiveness of the adopted methods are evaluated based on the using a performance statistical index. This is determined using the result of coefficient of determination is considered to examine the performance of the adopted algorithms as compared to the state of the art of the works. This can be obtained through, the coefficient of determination

Where n is the total number of observations, P X and X are represents forecasted and the observed values, respectively. The ultimate goal of this work is to assess the ability of the quadratic and cubic distribution to predict the COVID 19 through comparison of the other approaches, namely the logarithmic, logistic, curved, compound and exponential models.

In this section the descriptive and inferential results are cogently addressed. First, the descriptive results are briefly explained. Following this, the inferential results assisted through the predictive models are succeeded. Within this data two potential variables (confirmed cases and deaths) are taken into consideration for this study.

This study reveals about 1048, 156 confirmed cases and 55,143 deaths are taken into account worldwide, from the global assessment this is considered as very high risk. From the descriptive results, we noted that the spatial pattern of confirmed number cases of COVID-19 and deaths are both varying with high spread. This entails the distribution of this novel virus across worldwide both in death rate and confirmed cases is varying almost all countries in the world. We rely on the daily reported aggregated data across the globe on two main predictors: the confirmed number cases and deaths. We emphasize the significance of the confirmed cases that is not J o u r n a l P r e -p r o o f included in public and private media as broadly as the confirmed cases or the deaths. Therefore, completely two data distributions display an increment in America, the patterns of the deaths were also increased in European (Figure 1 ).

A spatial distribution of COVID 19 across the world is varying and growing up alarmingly. As 

To predict confirmed number of infections of COVID 19, additionally we considered several prediction approaches. We create predicts using some prediction models with an exponential family [48, 49] . This finding has entailed showing good prediction precision over different prediction mechanisms [50] [51] . Exponential related approaches can take into consideration a variability of pattern and prediction so as to accurately predict the confirmed cases and deaths, then we can give a due a attention for Africa to mitigate its spatial and temporal distributions.

Remarkably, unless we give a strong care in combating its mitigation, and develop an effective method, the pattern of this virus will remain danger for human beings even in the future. The proposed method also resembles to the other modeling methods to COVID 19 through an S-Curve algorithms (logistics curve, logarithmic, exponential, compound and the growth curves) that assumes convergence. Thus, all these approaches can be assessed using two different datasets related to COVID 19 on confirmed cases and death rates.

Based on the January 27, 2020, 2798 infected individuals had tested positive for coronavirus But, the estimated death rates are relying on the number infected patients to the total number of peoples died of the potential virus, which does not represent the real fact of the death rate.

Notably, the full denominator remains unknown because asymptomatic cases or patients with very mild symptoms might not be tested and will not be identified. Such cases therefore cannot be included in the estimation of actual mortality rates; since actual estimates pertain to clinically apparent COVID-19 cases ( Figure 2 ). The spatial distribution of COVID 19 In the study area is more and more. This figure cogently illustrates as the temporal distribution of COVID 19 is going very fast and alarmingly (See Figure 2 ). This period also based on the recent works [12, 13, 14, 15] , and by Read et al. [21] . Specifically, Zhao and coworkers [20] more pointed out on the assessments under-reporting rate of coronavirus number of cases, through building the epidemic growing curves such as an exponential growing Poisson process. We repeated the process as aforementioned but this can be attained through fitting modeling the data on confirmed number cases between January 27 th and 3 rd April 2020 (Fig. 3) 

There are several adopted predictive approaches to examine prediction of confirmed cases due to the impending influence of COVID 19. In this section, the performance of the Cubic, curved, quadratic, Logarithmic, Compound, Exponential and logistic are adopted to predict the confirmed cases and deaths. This entails as the shown in Table 1 that the presentation of cubic and quadratic are outperformed as compared methods in based on coefficient of determination.

This finding has shown that the proposed algorithm can enhance the variables of the cubic approach efficiently and producing a convenient result through the result attained from the performance measures (Table 1) . 

The descriptive result has pointed out as there is still temporal variation of death of COVID 19

with an alarm growing. The temporal distribution of death rate is exponentially increasing across the globe, from which a serious attention is required to mitigate its expansion in Africa in the future. It is an amazing growth especially temporally from month to month with an alarm growth ( Figure 4 ). But several adopted approaches are also implemented its prediction of the death rate, from which we can note that cubic and quadratic approaches are both better fit the death related data as compared to the other exponential families. The adopted models has shown that the COVID-19 disease infection rate increases more than an exponentially. Our findings is more resembled to the latest work which has shown that the model converges to a maximum number as time increases, indicating a limited impact of COVID-19 [39] . But, the results attained from our work indicates, the deaths is increasing in cubic and quadratic form as shown in Figure 5 . Brandon Michael et al [41] based on Meta findings or analysis, with more than 1389 COVID-19 patients, of which 273 (19.7%) are considered as severe disease [41] [42] [43] [44] [45] . Moreover, Bin Zhao, et al., [46] has suggested as the results of the sensitivity analysis show that the time it takes for a suspected population to be diagnosed as a confirmed population can have a significant impact on the peak size and duration of the cumulative number of diagnoses. Xinmiao Rong et al [47] proposed a new algorithm, from which a sensitivity findings and the simulations results reveals that, enhancing the ratios of timely judgment and shortening the waiting time for diagnosis cannot eradicate COVID 19. This notes, the death and spatial confirmed cases of COVID 19 across the globe is growing in alarm way, the African must give a care and work on its mitigations. 

The performance evaluation of the prediction models relying through comparing the findings obtained between adopted methods and other models to predict the deaths as shown in Table 2 . It can be concluded that the cubic and quadratic approaches are both outperforms to the other exponential families. Furthermore, the cubic method has the largest coefficient of determination indicating the observed values and the fitted curve are almost overlapping as compared to all the other comparison algorithms (quadratic, logistic, logarithmic, compound, growth and exponential), and pointing with high quality in predicting the death rates. Meanwhile, the quadratic models suggested and ranked as the 2 nd , which indicates superior results as compared to the other algorithms. Additionally, the results obtained from the coefficient of determination has again shown that good relationship between the predicted attained by the adopted methods and the observed confirmed cases of COVID 19, which is almost 0.994. Thereby, it also entails as the result is more consistent to the results attained from Figure 5 , which illustrates the data through the proposed algorithms based on the historical data of the COVID-19 death rate.

Therefore, for an abnormal growth of cases like the COVID 19, it can be suggested that the cubic mathematical approach has a high potential and more robust to predict the COVID 19 relied dataset. 

In this work, numerous concerns are addressed expressly, in the facet of the source of the outbreak, characteristics and symptoms of the patients, and also the pattern along with the spatial and temporal distribution indicates a strong remark for the developing countries. The result from the descriptive below has indicated as the COVID 19 patient above 40 years old needs a strong care especially in Africa as the infection fatality ratio in increasing exponentially ( Figure 6 ).

This is also strongly supported as the age of 42 In this discussion section, following these descriptive results, the prediction rates of some potential predictive factors are also addressed as given in the following table ( 

Infection Fatality Ratio in 1000 J o u r n a l P r e -p r o o f result shown in [3] has shown that a significantly high risk is observed with blood group A for COVID-19 with 1.279 odd ratio (95% CI 1.136~1.440), while a low risks of blood type O for COVID 19 with 0.680 odd ratios along with the confidence interval of (95% CI 0.599~0.771).

However, [10] noted as yet in the initial days of the outbreak of COVID 19 and there are a number of uncertainties in both the measure of the current outbreak, as well as the main epidemiological data concerning transmission. But, the speed and the rapidity of the progress of cases since the acknowledgement of the outbreak are much superior to that observed in outbreaks of the other virus. This is reliable with our larger prediction of the generative number for this outbreak compared to these other emergent coronaviruses, suggesting that containment or control of this virus may be significantly more problematic. To tackle the spread of this novel virus, [11] revealed as the 2019-nCoV infection was of clustering onset, are more likely to affect older males with comorbidities, and can result in severe and even fatal respiratory diseases such as acute respiratory suffering syndrome. In addition to this, the middle ages of those who expired in Italy was 81 years while about greater than more than two thirds of these infected patients had heart pressure, diabetes, HIV infected patients, cardiovascular diseases, or cancer, or were former smokers [18] . This 49-5.61 ). In addition, medical staff had a lower fatality rate than non-clinical staff for COVID-19 (RR = 0.12; 95% CI, 0.05-0.30) [22, 23, 24] (Table 3) . providers, and elders. Additionally, the African guideline must prepare strong guideline on its medical staff, healthcare providers, and public health individuals and researchers, which is more resembled as in [28, 53] . The growth prediction rate of confirmed cases and death rate of COVID 19 is seems more cubic and quadratic, as such as the distribution of this growing up alarmingly the joint team is needed for Africans to give a strong awareness for African community. Strong attention is highly recommended especially for an early death cases of COVID-19 outbreak occurred primarily in elderly people, possibly due to a weak immune system that permits faster progression of viral infection [28, 29] . Thus, Africans must give a due attention in these areas where highly spreading is occurred to mitigate the expansion of this potential virus, and giving an aggressive response to this virus is the most fundamental issue. Physical contact with wet and contaminated objects should be considered in dealing with the virus as an alternative route of transmission [31, 32] . As it is observed in Wuhan, China and we also observed in another countries counting the US have implemented major preventing and important controlling actions J o u r n a l P r e -p r o o f including travel screenings to control additional distribution of the virus [33] , as such all African countries must come together jointly to strongly work on mobility and tackle the spreading of the novel virus through a strong prevention and controlling mechanisms. Epidemiological changes in COVID-19 infection should be monitored taking into account potential routes of transmission and subclinical infections. To diminish fright and monetary harm, and to cogently accomplish and save the infection, much continue to be done in mitigating the spatial and temporal distribution of COVID 19. The Africans must work in cooperation to reduce its distribution so to disruption the transmission shackle of COVID 19. This will also entails to bring an efficient and an effective program to trace, diagnose, and cure an infected patients needs a due attention with regard to patient's previous health history, travelling experience, different blood types. It is of great authoritative that we call for global action to deal with this major public health emergency.

This research is proposed the predictive models on COVID 19 in Africa. Thereby, it also assesses the spatial and temporal pattern of the COVID 19 so that strong intervention is needed for an action to be taken. The result has shown that the spatial and temporal distribution of 

Association Of Abo And Rh Blood Group Types To Hepatitis B, Hepatitis C, Hiv And Syphilis Infection, A Five Year' Experience In Healthy Blood Donors In A Tertiary Care Hospital

Human susceptibility and resistance to Norwalk virus infection Nat Med

Relationship between the ABO Blood Group and the COVID-19 Susceptibility

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia inWuhan, China:a descriptive study

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia

Emerging Coronavirus 2019-nCoV Pneumonia

Analysis of clinical features of 29 patients with 2019 novel coronavirus pneumonia

Clinical Characteristics of 138 Hospitalized Patients With 2019 Novel Coronavirus-Infected Pneumonia in Wuhan

Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study

Incubation period of 2019 novel coronavirus (2019-nCoV) infections among travellers from Wuhan, China

Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China

COVID-19)

Time to use the p-word? Coronavirus enter dangerous new phase

The Economist. Tourism flows and death rates suggest covid-19 is being under-reported

COVID-19 and Italy: what next? March

An updated estimation of the risk of transmission of the novel coronavirus

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A datadriven analysis in the early phase of the outbreak

Novel coronavirus 2019-nCoV: Early estimation of epidemiological parameters and epidemic predictions

Risk Factors for Fatal Middle East Respiratory Syndrome Coronavirus Infections in Saudi Arabia: Analysis of the WHO Line List

Clinical determinants of the severity of Middle East respiratory syndrome (MERS): a systematic review and meta-analysis

1.Case fatality of SARS in mainland China and associated risk factors. Tropical medicine & international health: TM & 16 IH 2009: 21-7.16

Association of radiologic findings with mortality of patients infected with 2019 novel coronavirus in

Bin Cao Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

Estimates of the severity of coronavirus disease 2019:a model-based analysis

A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version)

Updated understanding of the outbreak of 2019 novel coronavirus (2019-nCoV) in Wuhan

Early transmission dynamics in wuhan, China, of novel coronavirus-infected pneumonia

Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study

A major outbreak of severe acute respiratory syndrome in Hong Kong

Novel wuhan (2019-nCoV) coronavirus

Rabeea Siddique: COVID-19 infection: Origin, transmission, and characteristics of human coronaviruses: Journal of Advanced Research

A Time-dependent SIR model for COVID-19 with Undetectable

Complete genome characterization of a novel coronavirus associated with severe human respiratory disease in Wuhan, China

Another decade, another coronavirus

HEMALATHA: COVID-19 (Novel Coronavirus 2019) -recent trends: SARS

Diagnosis Number Prediction Model in China medRxiv preprint doi

Early dynamics of transmission and control of COVID-19: a mathematical modeling study: Published by Elsevier Ltd

Chronic kidney disease is associated with severe coronavirus disease 2019 (COVID-19) infection: International Urology and Nephrology

Clinical characteristics of coronavirus disease 2019 in China

Clinical and biochemical indexes from 2019-nCoV infected patients linked to viral loadsb and lung injury

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan

Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan

Mathematical Modeling and Epidemic Prediction of COVID-19 and Its Significance to Epidemic Prevention and Control Measures: Annals of Infectious Disease and Epidemiology

Effect of delay in diagnosis on transmission of COVID-19

A state space framework for automatic forecasting using exponential smoothing methods

Exponential smoothing with a damped multiplicative trend

The accuracy of extrapolation (time series) methods: Results of a forecasting competition

The M4 competition: 100,000 time series and 61 forecasting methods

The M3-Competition: results, conclusions and implications

The Impacts of Covariates on Spatial Distribution of CORONA Virus 2019 (COVID-19): WHO Data through Multivariate Analysis of Covariance (MANCOVA)

Please check the following as appropriate: