This work introduces a deterministic model of COVID-19 spread aimed at analyzing non-pharmaceutical interventions in Kenya. The model accounts for symptomatic, asymptomatic and environmental transmission. Using the SEIR (Susceptible-Exposed- Infected-Recovered) compartmental model with additional component of the pathogen, the dynamics of COVID-19 outbreak is simulated while focussing on the impact of different control measures in the reduction of the basic reproduction number. The resulting system of ordinary differential equations (ODEs) is solved using a combination of fourth and fifth-order Runge-Kutta methods. Simulation results indicate that non-pharmaceutical measures such as school closure, social distancing and movement restriction emphatically flatten the epidemic peak curve by reducing the basic reproduction number.

Basic reproduction number, COVID-19, Intervention, SARS-CoV-2, SEIR-P, Simulation

Corona Virus Disease (COVID-19) is an infectious disease that is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The first case was reported in main-land China, City of Wuhan, Hubei on 29^{th} December 2019 [1]. Later on, the novel disease spread to countries in contact with China resulting in World Health Organization (WHO) declaring it as a Public Health Emergency of International Concern (PHEIC) on 30 January 2020 World Health Organisation [2]. As of 27^{th} July 2020 the number of reported confirmed cases globally had exceeded sixteen million [3] with Africa recording approximately seven hundred thousand of those cases. The 1^{st} case in Kenya was reported on the 13^{th} of March 2020 [4] and currently there are close to twenty thousand confirmed cases [3].

The three main types of infectiousness of COVID-19 are asymptomatic, pre-asymptomatic and symptomatic. The incubation period for COVID-19 in the pre-asymptomatic infectious group, which is the time between exposure to the virus (becoming infected) and symptom onset, is on average 5-6 days, however it can be up to 14 days. During this period some infected persons can be contagious. In a symptomatic COVID-19 case, the disease manifests itself with signs and symptoms. Asymptomatic transmission refers to transmission of the virus from a person, who does not develop symptoms [5]. The basic reproductive number (*R*_{0}), defined as the average number of secondary infections produced by a typical case of an infection in a population where everyone is susceptible [6], is affected by the rate of contacts in the host population, the probability of infection being transmitted during contact and the duration of infectiousness. The COVID-19 *R*_{0} in Kenya ranges from 1.78 to 3.46 [7]. The outbreak is thought to spread through a population via direct contact with infectious individuals [8] or cross transmission through pathogen contamination in the environment. Specifically, the virus is primarily spread between people during close contact, often via small droplets produced by coughing, sneezing, or talking [5,6,9,10,11]. After breathing out these droplets usually fall to the ground or on to surfaces rather than remain in the air over long distances [6,7,9]. People may also become infected by touching a contaminated surface and then touching their eyes, nose, or mouth [9,10]. The virus can survive on surfaces for up to 72 hours [11].

This kind of transmission has had adverse social and economic impact because all segments of the population are significantly affected. The disease is particularly detrimental to vulnerable social groups such as older age-groups, persons with disabilities and people with other underlying diseases. If the pandemic is not properly addressed through policy, the social crisis generated from COVID-19 could also increase inequality, exclusion, discrimination and global unemployment in the medium and long term United Nations: Department of Economic and Social Affairs Inclusion [12]. Inorder to achieve suitable control measures of COVID-19, a Susceptible-Exposed-Infected-Recovered (SEIR) mathematical prediction model is required. Prediction trends from the model are largely influenced by policy and social responsibility of a given country [13]. These trends enable us to understand how SARS-CoV-2 could spread across a given population and inform control measures that might mitigate future transmissions [11]. Nonpharmaceutical interventions based on sustained physical distancing have a strong potential to lower and flatten the epidemic peak in the absence of a vaccine [14]. Premature and sudden lifting of such interventions could lead to an earlier secondary peak [15].

An epidemiological study was done on a human-pathogen model that incorporated individuals with robust immunity in its compartments; the model examined human behavior during the disease outbreak, such as ignorance of social distancing so as to advise health care workers (HCW) [16]. It was established that nonpharmaceutical interventions play a major role in flattening the epidemic peak.

According to the WHO/China Joint Mission Report, 80% of laboratory-confirmed cases in China up to 20 February 2020 have had mild to moderate disease including both non-pneumonia and pneumonia cases; whilst 13.8% developed severe disease and 6.1% developed to a critical stage requiring intensive care [9]. It is therefore paramount to come up with an epidemiological model that factors in hospitalization of the mild to moderate cases and severity of the disease transmission.

In this study we explore a human-pathogen model mathematical model with infectious groups, hospitalized population, human population in intensive care unit and the fatalities. The study further investigates the effects of lifting non-pharmaceutical interventions such as school closure, physical distancing and lockdown on the model. It is envisaged that the dynamics of the results shall inform health experts in their research of efficient and effective ways of flattening the curve.

In this study a human-pathogen SEIR-P model for COVID-19 outbreak is formulated. The model considers a short-time period thereby ignoring the demographics of births and natural death. The total human population is subdivided into the susceptible human beings *S(t)*, the exposed human beings *E(t)*, the asymptomatic infectious population *I _{A}(t)*, the symptomatic infectious population

The model in Figure 1 culminates to a ten dimensional system of ordinary differential equations as follows;

Figure 1: Compartmental model of the SEIR model of transmission of COVID-19. View Figure 1

$$\frac{dS}{dt}=-\frac{{\beta}_{1}SP}{N}-\frac{{\beta}_{2}S({I}_{A}+{I}_{M}+{I}_{C})}{N}-\frac{{\beta}_{3}S(U+H)}{N},$$

$$\frac{dE}{dt}=\frac{{\beta}_{1}SP}{N}+\frac{{\beta}_{2}S({I}_{A}+{I}_{M}+{I}_{C})}{N}+\frac{{\beta}_{3}S(U+H)}{N}-\omega E,$$

$$\frac{d{I}_{A}}{dt}={\delta}_{A}\omega {\rm E}-({\tau}_{A}+{\gamma}_{A}){I}_{A},$$

$$\frac{d{I}_{M}}{dt}=(1-{\delta}_{A}-{\delta}_{C})\omega E-{\tau}_{M}{I}_{M},$$

$$\frac{d{I}_{C}}{dt}={\delta}_{C}\omega E-({\tau}_{C}+{\gamma}_{C}){I}_{C},\text{(2}\text{.1)}$$

$$\frac{dH}{dt}={\tau}_{A}{I}_{A}+{\tau}_{M}{I}_{M}+{\tau}_{C}{I}_{C}+\varphi U-(\xi +{\lambda}_{H}+{\gamma}_{H})H,$$

$$\frac{dU}{dt}=\xi H-(\varphi +{\lambda}_{U})U,$$

$$\frac{dD}{dt}={\lambda}_{H}H+{\lambda}_{U}U+{\lambda}_{C}{I}_{C},$$

$$\frac{dR}{dt}={\gamma}_{H}H+{\gamma}_{A}{I}_{A},$$

$$\frac{dP}{dt}={\eta}_{1}({I}_{A}+{I}_{M}+{I}_{C})+{\eta}_{2}(U+H)-{\mu}_{P}P.$$

with the initial conditions:

*S*(0) > 0, *E*(0) > 0, *I _{A}*(0) = 2,

*H*(0) = 0, *U*(0) = 0, *D*(0) = 0, *R*(0) = 0, *P*(0) = 5000. (2.2)

The terms $\frac{{\beta}_{1}SP}{N},\text{}\frac{{\beta}_{2}S({I}_{A}+{I}_{M}+{I}_{C})}{N}$ and $\frac{{\beta}_{3}S(U+H)}{N}$ denote the rate at which the susceptible individuals *S(t)*, gets infected by the pathogens from environment (*P(t)*), infectious humans (*I _{A}(t)*,

*I _{A}(t)* may join the recovery group

Table 1: Description of Model Parameters. The parametric values are determined based on the references [5,7,14-19]. View Table 1

In the Disease Free Equilibrium (DFE) there are no infections from the humans nor the pathogens resulting to *S* = *N*. This implies that *P* = *I _{A}* =

Let *x = (E, I _{A}, I_{M} , I_{C}, H, U, P )^{T}* then the model can be written as

$$\frac{dx}{dt}=F(x)-V(x),$$ where

$$F(x)=\left(\begin{array}{l}{\beta}_{1}P+{\beta}_{2}({I}_{A}+{I}_{M}+{I}_{C})+{\beta}_{3}(U+H)\\ \text{}0\\ \text{}0\\ \text{}0\\ \text{}0\\ \text{}0\\ \text{}{\eta}_{1}({I}_{A}+{I}_{M}+{I}_{C})+{\eta}_{2}(U+H)\end{array}\right)\text{(3}\text{.1)}$$

$$V(x)=\left(\begin{array}{l}\text{}\omega E\\ \text{}({\tau}_{A}+{\gamma}_{A}){I}_{A}-{\delta}_{A}\omega E\\ \text{}({\delta}_{A}+{\delta}_{C}-1)\omega E+{\tau}_{M}{I}_{M}\\ \text{}({\tau}_{C}+{\lambda}_{C}){I}_{C}-{\delta}_{C}\omega E\\ (\xi +{\lambda}_{H}+{\gamma}_{H})H-{\tau}_{A}{I}_{A}-{\tau}_{M}{I}_{M}-{\tau}_{C}{I}_{C}-\varphi U\\ \text{}(\varphi +{\lambda}_{U})U-\xi H\\ \text{}{\mu}_{P}P\end{array}\right)\text{(3}\text{.2)}$$

Evaluating the derivatives of F and V at the Disease Free Equilibrium yields matrix F and V as follows;

$$F\text{}=\text{}\left(\begin{array}{l}\text{}0\text{}{\beta}_{2}\text{}{\beta}_{2}\text{}{\beta}_{2}\text{}{\beta}_{3}\text{}{\beta}_{3}\text{}{\beta}_{1}\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}{\eta}_{1}\text{}{\eta}_{1}\text{}{\eta}_{1}\text{}{\eta}_{2}\text{}{\eta}_{2}\text{}0\end{array}\right)\text{(3}\text{.3)}$$

$$V\text{}=\text{}\left(\begin{array}{l}\text{}\omega \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}-{\delta}_{A}\omega \text{}{\tau}_{A}+{\gamma}_{A}\text{}0\text{}0\text{}0\text{}0\text{}0\\ ({\delta}_{A}+{\delta}_{C}-1)\omega \text{}0\text{}{\tau}_{M}\text{}0\text{}0\text{}0\text{}0\\ \text{}-{\delta}_{C}\omega \text{}0\text{}0\text{}{\tau}_{C}+{\lambda}_{C}\text{}0\text{}0\text{}0\\ \text{}0\text{}-{\tau}_{A}\text{}-{\tau}_{M}\text{}-{\tau}_{C}\text{}\xi +{\lambda}_{H}+{\gamma}_{H}\text{}-\varphi \text{}0\text{}\\ \text{}0\text{}0\text{}0\text{}0\text{}-\xi \text{}-\varphi +{\lambda}_{U}\text{}0\text{}\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}{\mu}_{P}\end{array}\right)\text{(3}\text{.4)}$$

Substituting, *λ _{C} + τ_{C} = C_{2}, τ_{A} + γ_{A} = C_{3} , (φ + λ_{U} )-ξφ = C_{6} -ξφ = C_{4}, ξ + λ_{H} + γ_{H} = C_{5}, φ + λ_{U} = C_{6} and (ξ + λ_{H} + γ_{H} ) (φ + λ_{U} ) = C_{5}C_{6} - ξφ = C_{1}* in matrix V yields

$$V\text{}=\text{}\left(\begin{array}{l}\text{}\omega \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}-{\delta}_{A}\omega \text{}{C}_{3}\text{}0\text{}0\text{}0\text{}0\text{}0\\ ({\delta}_{A}+{\delta}_{C}-1)\omega \text{}0\text{}{\tau}_{M}\text{}0\text{}0\text{}0\text{}0\\ \text{}-{\delta}_{C}\omega \text{}0\text{}0\text{}{C}_{2}\text{}0\text{}0\text{}0\\ \text{}0\text{}-{\tau}_{A}\text{}-{\tau}_{M}\text{}-{\tau}_{C}\text{}{C}_{5}\text{}-\varphi \text{}0\text{}\\ \text{}0\text{}0\text{}0\text{}0\text{}-\xi \text{}{C}_{6}\text{}0\text{}\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}{\mu}_{P}\end{array}\right)\text{(3}\text{.5)}$$

The inverse of matrix V is determined as

$${V}^{-1}\text{}=\text{}\left(\begin{array}{l}\text{}\frac{1}{\omega}\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}\frac{-{\delta}_{A}}{{C}_{3}}\text{}\frac{1}{{C}_{3}}\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}-\frac{(c+{\delta}_{C}-1)\text{}}{{\tau}_{M}}\text{}0\text{}\frac{1}{{\tau}_{M}}\text{}0\text{}0\text{}0\text{}0\\ \text{}-\frac{{\delta}_{C}}{{C}_{2}}\text{}0\text{}0\text{}\frac{1}{{C}_{2}}\text{}0\text{}0\text{}0\\ \text{}\frac{-{C}_{6}((({\delta}_{A}+{\delta}_{C}-1){C}_{3}-{\delta}_{A}{\tau}_{A}){C}_{2}-{\tau}_{C}{C}_{3}{\delta}_{C})}{{C}_{2}{C}_{3}{C}_{1}}\text{}\frac{{\tau}_{A}{C}_{6}}{{C}_{3}{C}_{1}}\text{}\frac{{C}_{6}}{{C}_{1}}\text{}\frac{{\tau}_{C}{C}_{6}}{{C}_{2}{C}_{1}}\text{}\frac{{C}_{6}}{{C}_{1}}\text{}\frac{\varphi}{{C}_{1}}\text{}0\text{}\\ \text{}\frac{-\xi ({C}_{2}(1-{\delta}_{A})-{\tau}_{C}{\delta}_{C}}{{C}_{3}{C}_{2}{C}_{1}}\text{}\frac{\xi {\tau}_{A}\text{}}{{C}_{3}{C}_{1}}\text{}\frac{\xi \text{}}{{C}_{1}}\text{}\frac{\xi {\tau}_{C}\text{}}{{C}_{2}{C}_{1}}\text{}\frac{\xi \text{}}{{C}_{1}}\text{}\frac{{C}_{5}}{{C}_{1}}\text{}0\text{}\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}\frac{1}{{\mu}_{P}}\end{array}\right)\text{(3}\text{.6)}$$

The product FV^{-1} that results to;

$$F{V}^{-1}\text{}=\text{}\left(\begin{array}{l}\text{}{C}_{7}\text{}{C}_{9}\text{}{C}_{11}\text{}{C}_{13}\text{}{C}_{15}\text{}{C}_{17}\text{}{C}_{19}\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\text{}0\\ \text{}{C}_{8}\text{}{C}_{10}\text{}{C}_{12}\text{}{C}_{14}\text{}{C}_{16}\text{}{C}_{18}\text{}0\end{array}\right)\text{(3}\text{.7)}$$

where

$${C}_{7}=\frac{{\beta}_{2}{\delta}_{A}}{{C}_{3}}+\frac{{\beta}_{2}(-{\delta}_{A}-{\delta}_{C}+1)}{{\tau}_{M}}+\frac{{\beta}_{2}{\delta}_{C}}{{C}_{2}}-\frac{{\beta}_{3}{C}_{6}(({\delta}_{A}+{\delta}_{C}-1){C}_{3}-{\delta}_{A}{\tau}_{A}){C}_{2}-{\tau}_{C}{C}_{3}{\delta}_{C})+{\beta}_{3}((({\delta}_{A}+{\delta}_{C}-1){C}_{3}-{\delta}_{A}{\tau}_{A}){C}_{2}-{\tau}_{C}{C}_{3}{\delta}_{C})\xi}{{C}_{2}{C}_{3}{C}_{1}}$$

$${C}_{8}=\frac{{\eta}_{1}{\delta}_{A}}{{C}_{3}}+\frac{{\eta}_{1}(-{\delta}_{A}-{\delta}_{C}+1)}{{\tau}_{M}}+\frac{{\eta}_{1}{\delta}_{C}}{{C}_{2}}-\frac{{\eta}_{2}{C}_{6}(({\delta}_{A}+{\delta}_{C}-1){C}_{3}-{\delta}_{A}{\tau}_{A}){C}_{2}-{\tau}_{C}{C}_{3}{\delta}_{C})+{\eta}_{2}((({\delta}_{A}+{\delta}_{C}-1){C}_{3}-{\delta}_{A}{\tau}_{A}){C}_{2}-{\tau}_{C}{C}_{3}{\delta}_{C})\xi}{{C}_{2}{C}_{3}{C}_{1}}$$

$${C}_{9}=\frac{{\beta}_{2}}{{C}_{3}}+\frac{{\beta}_{3}{\tau}_{A}{C}_{6}}{{C}_{3}{C}_{1}}+\frac{{\beta}_{3}\xi {\tau}_{A}}{{C}_{3}{C}_{1}}$$

$${C}_{10}=\frac{{\eta}_{1}}{{C}_{3}}+\frac{{\eta}_{2}{\tau}_{A}{C}_{6}}{{C}_{3}{C}_{1}}+\frac{{\eta}_{2}\xi {\tau}_{A}}{{C}_{3}{C}_{1}}$$

$${C}_{11}=\frac{{\beta}_{2}}{{\tau}_{M}}+\frac{{\beta}_{3}{C}_{6}}{{C}_{1}}+\frac{{\beta}_{3}\xi}{{C}_{1}}$$

$${C}_{12}=\frac{{\eta}_{1}}{{\tau}_{M}}+\frac{{\eta}_{2}{C}_{6}}{{C}_{1}}+\frac{{\eta}_{2}\xi}{{C}_{1}}$$

$${C}_{13}=\frac{{\beta}_{2}}{{C}_{2}}+\frac{{\beta}_{3}{\tau}_{C}{C}_{6}}{{C}_{2}{C}_{1}}+\frac{{\beta}_{3}{\tau}_{C}\xi}{{C}_{2}{C}_{1}}$$

$${C}_{14}=\frac{{\eta}_{1}}{{C}_{2}}+\frac{{\eta}_{2}{\tau}_{C}{C}_{6}}{{C}_{2}{C}_{1}}+\frac{{\eta}_{2}{\tau}_{C}\xi}{{C}_{2}{C}_{1}}$$

$${C}_{15}=\frac{{\beta}_{3}{C}_{6}}{{C}_{1}}+\frac{{\beta}_{3}\xi}{{C}_{1}}$$

$${C}_{16}=\frac{{\eta}_{2}{C}_{6}}{{C}_{1}}+\frac{{\eta}_{2}\xi}{{C}_{1}}$$

$${C}_{17}=\frac{{\beta}_{3}\varphi}{{C}_{1}}+\frac{{\beta}_{3}{C}_{5}}{{C}_{1}}$$

$${C}_{18}=\frac{{\eta}_{2}\varphi}{{C}_{1}}+\frac{{\eta}_{2}{C}_{5}}{{C}_{1}}$$

$${C}_{19}=\frac{{\beta}_{1}}{{\mu}_{P}}Z\text{(3}\text{.8)}$$

*R*_{0} is the spectral radius of the product FV^{-1} that results to;

$${R}_{0}=\frac{{C}_{7}}{2}\pm \frac{\sqrt{4{C}_{8}{C}_{19}+{C}_{7}^{2}}}{2}\text{(3}\text{.9)}$$

Here *R*_{0} is the sum of three terms representing the average new infections contributed by each of the three infectious classes; pathogens, infectious groups (mild, asymptomatic and critical) and hospitalized groups denoted by *β*_{1}, *β*_{2} and *β*_{3} respectively.

Implementation of the NPIs is aimed at reducing the contacts within the population. Reduction of the contacts reduces the *R*_{0}, which is expressed in terms of the infection transmission rates *β*_{1}, *β*_{2} and *β*_{3} in the previous section. Since low rates of infection transmission implies a low value of *R*_{0} then we implement the non-pharmaceutical interventions (NPIs) by multiplying the rates of infection transmissions by a 20% and 30% factor. The interpretation is that the NPIs yield an overall mitigation of the epidemic spread by 20% and 30% respectively. Kenya instituted various mitigation strategies immediately the first case of COVID-19 was confirmed, therefore in this study the *R*_{0} at the beginning of epidemic is taken as 1.50. The *R*_{0} corresponding to 20% and 30% contact reduction are 1.28 and 1.17 respectively.

A simulations study was done using a total population of Kenya, N, as 4.76 × 10^{7}. Additionally, an identification rate *p* of new infectious cases was considered and determined as the ratio of confirmed cases over the total number of tested individuals. At the time of this study *p* was 0.022, while the parameter values were taken as shown in Table 1. The simulation was initialized at date 13-3-2020 and run for 365 days with the initial values *I _{A}*(0) = 2,

The results of the infectious (*I _{A}* +

Figure 2: Left panels depicts simulation of infectious and hospitalized cases, in unmitigated and mitigated situations. The implementation of NPIs results to a delay and significant reduction in the cases, as shown by the varying peaks. Right panels show the simulated cumulative cases for both infectious and hospitalized individuals. The infectious cases comprise of individuals in compartments *I _{A}*,

Figure 3: Left panel depicts the data of the confirmed cases in Kenya against the simulated infectious cases for the first 150 days, in unmitigated and mitigated situations. The simulated results of a 30% NPIs effectiveness coincides with the data. Right panel show the cumulative deaths, which decline significantly as the NPIs take effect. View Figure 3

Table 2: Simulated peaks of infectious and hospitalized cases, with the associated peak dates. The table depicts the predicted peak values of (daily) reported cases and corresponding times. The cases are placed in two categories, namely: infectious (*I _{A}* +

The control measures were put in place for the entire simulation time, so there was no rebound of the infections. A strong one-time social distancing measure would not sufficiently prevent overwhelming of the health care capacities, since it keeps a significant number of the population in susceptible compartment such that a rebound in infections, after the measure has been lifted, will lead to an epidemic wave that surpasses the capacity. Moreover, previous studies have shown that intermittent application of the NPIs maintains the infections within the health care capacities, but prolongs the total duration of the epidemic.

With absence of cure or vaccine for COVID-19 non-pharmaceutical that include measures such as contact tracing, quarantine, social distancing, wearing of face masks, and handwashing should be maintained. Mass testing and effective contact tracing has been a great challenge in low-income countries due to inadequate testing reagents and lack of proper coordination. Therefore, these countries have adopted social distancing measures such as closure of schools, reducing sizes of gatherings, minimizing contact in workplaces, and imposing curfews. The main aim of such interventions is to flatten the curve i.e. prevent infection from spreading fast and bring down the number of infected cases. Curve flattening is necessary because it avoids overwhelming the health systems and gives them time to develop treatment mechanisms.

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