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Course Title: Biology #2053

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A virus is a microscopic infective agent consisting of a nucleic acid molecule enveloped in a protein layer which is able to attach itself to the living body cells of a host and multiply itself. Omido (https://chs.uonbi.ac.ke/latest-news/what-coronavirus-and-how-can-it-be-tackled) et al. Coronaviruses (CoV-2) are an extensively large group of viruses that cause minor illnesses such as common cold and range to more serious diseases such as Middle East Respiratory Syndrome (MERS-CoV) and Severe Acute Respiratory Syndrome (SARS-CoV). COVID-19 is a new disease caused by a newly discovered strain of Coronavirus. Viruses cannot survive on their own and therefore they seek to find a susceptible cell on which they attach themselves and begin to multiply i.e. they are obligate pathogens. This means that they cannot survive in any other environment other than a host, in order to reproduce. Balloux and van Dorp et al. All viruses are obligate pathogens.

According to numerous debates by great scientists over time, there have come up three hypotheses which might be viable in the explanation of the origin of viruses. Wessner et al. The first hypothesis indicates that viruses may have arisen from genetic cell elements that gained the ability of movement between cells. The second one is the reduction hypothesis where cells are said to be remnants of other cells. The third hypothesis implies that viruses coexisted with their present cellular hosts. The novel coronavirus of 2019, that has brought about mayhem and wreaked havoc all over the world today is caused by a case of acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Lin et al. This virus, which is believed to have its origin in Wuhan, Hubei province of China, was initially reported to the Wuhan Municipal Health Centre as a case of pneumonia of unknown etiology in December 2019. By January 2020, the obligate pathogen had been identified, and afterwards, new infections were reported due to human transmission. Towards the end of January, almost all the provinces in China reported new cases of COVID-19. This was an outbreak that resulted into over 170,000 confirmed cases as well as more than 6,500 deaths all over the world. After numerous research done afterward, the “pneumonia of unknown etiology had become COVID-19. The disease is transmitted directly from person to person trough basic human interaction. (W.H.O) et al. The novel Coronavirus COVID-19 disease is majorly spread through saliva droplets or nose discharge released into the air when a sick person coughs or sneezes. Hence the incessant sensitization made to the general public as appertaining to respiratory hygiene.

Initially, the estimates during the outbreak of the virus in Wuhan, China, showed the doubling period of new infections, for the virus to be between 6 to 7 days with an R0 value of 2.2 to 2.7. R0 (R naught), also referred to as the reproduction number is a mathematical term that is used to illustrate how contagious an infectious disease is. Infections tend to reproduce themselves as they get transmitted to new hosts. The R0 value gives us the estimated number of people who will most likely get the disease once in contact with a sick person. This method of calculation is only applicable to a set group of people who were initially healthy and have not received any form of vaccination for that disease whatsoever. The incubation/infectious period (L) was found to be 4.2 days. Based on the results obtained from research done, during the first stages of COVID-19 in Wuhan, China, the doubling rate was found to be 2.3 to 3.3 days. Given that we assume the serial interval to be 6 to 9 days, then a median R0 value of 5.7 (95% CI 3.8–8.9) will be obtained. The R0 of a disease is the main factor to consider in order to find out how a disease will grow and subsequently affect a population and there are three possible scenarios: When  R0<1, then each existing infection has a viability of causing less than one new infection, therefore the virus will get transmitted less and eventually disappear. If R0=1, then each present infection is able to each existing infection causes one new infection. This implies a stability in the state of the virus which will not escalate further in the event of treatment. If R0>1, then each pre-existing case can cause several other infections therefore leading to an epidemic. Accurate R0 projections for COVID-19 are as illustrated here, https://covid19-projections.com/us-mo


Two modelling approaches were developed to compare the outbreak rate of COVID-19 in Wuhan, China to that of other places. In the first arrival model, the possibility of arrival times of cases outside Hubei were calculated as a function of gradual growth of the infected population in Wuhan before the end of January. Domestic travelling data was used in the computation of certain probabilities: the probability that an infected person from Wuhan travelled and if they did, the probability would be shown as a function of the number of infections yet to be known in Wuhan. In the case count model, persons infected from Wuhan but received diagnoses in other areas were accounted for and explicitly modelled using the susceptible-exposed-infectious-recovered (SEIR) model. This model was then added onto the daily case count data of new infections outside Hubei before any substantial transmissions happened to occur. The use of data collected outside the province avoided negative change in the integrity of surveillance for new cases. As soon as new cases got confirmed in other provinces, they had already received active testing kits and therefore the monitoring of the disease was amply dealt with.

COVID-19 pandemic was officially confirmed to have reached Missouri, in March 2020. By 20th July 2020, the Missouri Department of Health and Senior Services (MDHSS) had confirmed 33,624 active cases of the novel Coronavirus COVID-19, and 1,132 deaths in the state cumulatively. The (SEIR) epidemiological model was used in the estimation of the probable spread of the virus depending on the numbers of those who are susceptible to the virus, those who have been exposed and the ones that are already infected.  The disease’s reproductive rate is the main variable to be considered in this case. It is able to predict the future number of new infections based on the present cases. If the social distancing regulations set by the state were not adhered to, then the COVID-19 reproductive rate in the most populous county (St. Louis) would be around 3.3. An illustration of the social distancing effect was done using the parameters: low, moderate and high levels. Where the low levels gave reproductive rates of  approximately 2.5, the moderate gave 1.5, while high levels gave 0.9. This necessitated the order on St. Louis residents to strictly stay at home. The population density of a place was also taken into consideration, with areas having a lower population being assumed to harbor a certain degree of natural distancing compared to the more dense population areas. The data collected by the (CDC) Centre for Disease Control and Prevention, was used to ascertain the numbers of deaths and hospitalizations with reference to age distributions.


The state department through the Missouri Department of Health and Senior Services gave a mandatory order that all state residents maintain optimum levels of self-hygiene and social distancing on the 4th of April. Before this, the MDHSS had already put in place strategies that would enable enforcement of this order as of the previous month. By the end of April 2020, there had arisen, 7,562 new infections and 329 death cases as a result of the COVID-19. As a result of such great numbers recorded, it was realized that they had come about based on the population density of a certain area within the state. Counties like St. Louis, which harbored a great number of residents were at a greater risk of more infections rather than less populated counties. Social distancing levels were taken into account using terminologies such as low, moderate or high levels.

The map shown below predicts the effect of social distancing on the state of Missouri over six months, with reference to the present COVID-19 data collected so far. In the presence of high social distancing, the reproductive rate of the disease can be estimated to be around 0.9. This value is obtained based on the collective effects of the adherence to the “stay at home” orders at both state and county levels of governance. The moderate levels of social distancing would give a reproductive rate of 1.5  while in the case of low levels, we would achieve a value of 2.5. Without effecting the social distancing order we would achieve a reproductive rate of 3.3, which would be quite worrying.

 The SIR model is adjusted based upon a county’s populous nature, i.e. counties that had a lower population density were assumed to have a naturally occurring state of social distancing while high population counties required more individual effort in ensuring social distancing. The age distributions were used to make projections for the rates of death and new infections as well.

Figure 1: Sources: Case count and death data from 1point3acres.com and the Missouri Department of Health and Social Services; case counts by age from MO DHSS. Hospitalization rates and death rates calculated per COVID-positive case per age group based upon Centers for Disease Control published rates. Population data from the 2018 American Community Survey 5-Year Estimates. Population density calculated from the Area Health Resource File. Last updated 6/16/2020.

Below is a table showing projections of the rate of new infections, hospitalization of the patients and the deaths for 6 months.

 Low Social Distancing LevelsModerate Social Distancing LevelsHigh Social Distancing Levels
Projected Infections
St. Louis County (Urban)901,386586,8978,481
Jackson County (Urban)624,310402,3331729
Scott County (Suburban)33,32116,309110
Atchison County (Rural)2,71432
Projected Hospitalizations
St. Louis County83,61754,432786
Jackson County (Urban)54,06434,818150
Scott County (Suburban)3,0731,49810
Atchison County (Rural)28600
Projected Deaths
St. Louis County23,50515,325290
Jackson County13,7938,88648
Scott County8434113
Atchison County9400

Keenan and McKendrick et al. In the calculation of R0 value, we may use a basic epidemiological model (SIR model). Where SIR stands for: susceptible, infected and recovered. This model is deemed quite simple by researchers since it directly represents above mentioned 3 compartments. Exposed persons are initially termed as susceptible to the pathogen then they become infected with the virus and once a cure is found for the disease or their bodies develop natural immunity, they eventually recover from the disease. This model can be easier represented mathematically using ordinary differential equations. The below mentioned are three ODE’S that can be used:


  • β – transmission rate,
  • γ – recovery rate (or the inverse of the infectious period),
  • N – total population size such that N = S + I + R.
  • S – susceptible individuals
  • I – infected persons
  • R – recovered persons
  • t – time

The first standard ODE model assumes zero births and deaths.

When the pandemic began, (t = 0), which implies that there are purely susceptible persons in the population with only a single case of an infected person. If the rate of recovery (i.e., β/γ > 1), in this model goes beyond the rate of recovery from the disease, then it will spread. This can be shown as: (dI/dt > 0). β/γ can be used to represent the new cases per unit time, then multiplied by the infection period. It can be used to describe how many new infections came about as a result of the initial single infected person. In this SIR model, the basic reproduction number, R0 is equal to β/γ. The Kermack–McKendrick model was greatly overlooked by numerous scientists until late in the 1970s when Anderson and May used the model in studying and developing infectious disease control strategies. The basic reproductive number, R0  is an important parameter to gauge the dynamics of a disease as it is able to predict when an epidemic may occur or what would happen if its value exceeded unity, 1.

When the effective reproduction number, Re = R0 × (S/N) is greater than 1.0, it is imperative that the prediction we make is that there will be an increased spread of the disease. The resulting reproductive number shows that the less the number of susceptible individuals there are (S/N), the slower the rate of disease transmission will be. Epidemiologists generally consider the basic reproductive number as one of the most important parameters in the process of predictions of the direction in which a new disease may take and therefore know whether they might be able to contain it or curtail it’s spread. The mandate of the public health motivators in the event of a pandemic, is to curtail the spread of the disease by enforcement and implementation of set strategies. This is done by reducing: the rate of disease transmission, the infection period or even reduce RE by cutting down on the numbers of susceptible persons in the community. Therefore the need for closure of public gathering spaces, use of antiviral medication and increased vaccination to the members of the public.

Figure 2: Infection rate prediction by the R0 value based on simple models. Note. For simple models such as SIR and SEIR, the basic reproductive number of an epidemic offers insight into the rate of new infections. However, estimation of R0 often results in broad confidence intervals.
Figure 3: Comparison of the dynamics of an SIR model and an SEIR model. Note. SEIR = susceptible–exposed–infected–recovered and SIR = susceptible–infected–recovered. These models have identical values (specifically R0 = 1.5). The epidemic predicted by SIR model peaks earlier and has a higher peak incidence as well as shorter duration than the epidemic predicted by the SEIR model. These differences in dynamics simply reflect whether modelers believe there is a latent period for a virus. Consider: N = 1000; β = 0.1; γ = 0.0667; ν = 0.1.


The above findings point to the conclusion that the state of Missouri may be able to benefit from the social distancing regulations if adhered to the letter during this COVID-19 pandemic. This will offer a level of protectionism to the more vulnerable persons of the population. Despite the fact that high social distancing levels will have adverse effects on the economic and social status of the community, it will still go a long way in reducing the rate of new infections, the number of hospitalized individuals as well as those who succumb to and die from the disease in all Missourian counties. We cannot be able to predict for how long the current situation will be norm, in order to maintain the health of the inhabitants of Missouri but all we can do is hope that a vaccine is developed as soon as possible. Communities with individuals who are mainly of advanced ages may greatly benefit from social distancing practices. Recent researches have also shown that persons who have chronic conditions, stand a greater risk of infection by COVID-19 and hence the need for them to stay at home or stay away from crowds in the event that they really have to leave their houses. Rural places may manifest relatively lower infection rates due to their smaller numbers of inhabitants. If we are able to increase the participation of individuals in social distancing, we could reduce the new infection cases by a great extent. This may further lower the current projections on new infections, hospitalizations and deaths in the state. If that would occur then our health institutions would have a hard time in taking care of these patients. The burden on rural area hospitals would be reduced, as they have fewer resources. An influx in the numbers of COVID-19 new infections would burden them greatly due to their disproportionate nature of available resources. Quite obviously, the rural areas have less number of adjustable ICU beds as compared to the urbanized areas. The distribution is given at a ratio of 1.6:2.9 respectively. Wearing of masks while outdoors and policies on social distancing should be reinforced since they are of paramount importance among all the possible preventive measure being taken to reduce infections. Since the coronavirus COVID-19, is transmitted via interaction of body fluids i.e. saliva and mucus, then it is necessary that such measures are adhered to.

Taking into account the contact patterns of the population, the demographics the viable hosts and the specific obligate pathogen in question, the calculation of the basic reproductive ratio can prove quite difficult. Although the basic reproductive number, R0 is very important in the study and prediction of the outbreak of a disease, it can prove quite useless once we need to determine the potential success of the set measures for controlling the spread of the disease. If  R0 is taken into account among other characteristics of a disease, then it can be important in the decision-making process of our health fraternities.

The researchers and scientists who model the projections for the infectious diseases may be able to understand and appreciate the concepts discussed herein but those who may be tasked with the implementation and response during such pandemics may have little or relatively no knowledge concerning the same. This is quite disadvantageous as they may end up making wrong decisions during such times of great tension. If the estimated values of the basic reproductive number, R0 will be used to determine health response strategies, then the policy makers should be notified of the same and receive all relevant information about it. Apart from the people dedicated to calculating the basic reproductive number, R0, resources must in like manner be devoted to estimating other parameters of an outbreak like transmission rates, infectious periods, or latent periods. Tuite et al. The analysis made can serve as a definitive threshold of analysis while taking into consideration the basic reproductive number R0, as well as other necessary parameters. More evidence collected with reference to the population density of the state and how the basic reproductive number affects COVID-19, is key to take into account while studying what should be done to ascertain the public health response and study of the population in Missouri.



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