The global population has been and will continue to be severely impacted by the COVID19 epidemic. The primary objective of this research is to demonstrate the future impact of COVID19 on those who suffer from other fatal conditions such as cancer, heart disease, and diabetes. Here, using ordinary differential equations (ODEs), two mathematical models are developed to explain the association between COVID19 and cancer and between COVID19 and diabetes and heart disease. After that, we highlight the stability assessments that can be applied to these models. Sensitivity analysis is used to examine how changes in certain factors impact different aspects of disease. The sensitivity analysis showed that many people are still nervous about seeing a doctor due to COVID19, which could result in a dramatic increase in the diagnosis of various ailments in the years to come. The correlation between diabetes and cardiovascular illness is also illustrated graphically. The effects of smoking and obesity are also found to be significant in disease compartments. Model fitting is also provided for interpreting the relationship between real data and the results of this work. Diabetic people, in particular, need to monitor their health conditions closely and practice heart health maintenance. People with heart diseases should undergo regular checks so that they can protect themselves from diabetes and take some precautions including suitable diets. The main purpose of this study is to emphasize the importance of regular checks, to warn people about the effects of COVID19 (including avoiding healthcare centers and doctors because of the spread of infectious diseases) and to indicate the importance of family history of cancer, heart diseases and diabetes. The provision of the recommendations requires an increase in public consciousness.
Epidemiology is a field of study that analyzes the causes of illness and health by addressing the facts of a population [
A new field derived from the interaction between epidemiology and genetics over many years has emerged, named genetic epidemiology. This new area concentrates on the connection between genetic and environmental parameters during disease in a human population. Genetic epidemiology provides benefits for comprehending the interaction between genetic roots and major chronic disorders like coronary heart disease, cancer, and diabetes [
Cardiovascular disorders (CVDs) belong to the category of disorders involving blood veins and the heart. There are a variety of cardiovascular disorders (CVDs), including coronary heart, cerebrovascular, peripheral arterial, rheumatic heart, congenital heart, pulmonary embolism and deep vein thrombosis [
Coronary heart disease (CHD) is a disorder of the blood veins providing for the heart muscle [
Cancer is another disorder that includes a wide group of diseases. It describes unpreventable abnormal or damaged cell growth almost anywhere in human parts or organs. Cancer does not differentiate between age, gender, family background, or other categories. However, cancer statistics enable us to recognize the similarities and differences between categories identified with sex, age, ethnic groups, etc. The mathematical model proposed in [
According to the basic cancer facts, there are plenty of factors that raise the risk of having cancer. High tobacco use, high alcohol use, and being overweight are some of these factors. These factors are alterable within the realm of possibility. On the other hand, other risk factors are not modifiable, like inherited genetic mutations [
Diabetes mellitus, simply called diabetes, is a disease caused by insufficient insulin production by the pancreas. It leads to uncontrolled amounts of glucose or sugar in the human body [
Mathematical models allow us to foresee the future outcomes of an epidemic or health issues. Besides this, they might be used as interpretive tools for the clarification of basic principles of transmission or extension [
In recent years, the most wellknown contagious disease has been the Coronavirus disease (COVID19), caused by the SARSCoV2 virus. COVID19 is transmitted by liquid particles from the mouth or nose of an infected person. It is categorized as a pandemic disease since it affects many countries within international boundaries [
In recent years, many articles have been published in mathematical modeling that analyze COVID19. Article [
Mathematical modeling plays a remarkable role in infectious (epidemic/pandemic) diseases and chronic diseases. In the health sciences, mathematical models can be applied to identify the dynamics and aspects of diseases such as cancer, coronary heart disease (CHD), and diabetes. In [
This paper aims to present the effect of COVID19 on other significant diseases, specifically cancer, heart disease, and diabetes. This study aims to warn people and increase their awareness so that the necessity of doctor and hospital visits in the upcoming years can be reduced. The presented study has a significant role in health sciences by being one of the strong and rare models that discuss the effect of the COVID19 pandemic from different and serious perspectives. On that note, two mathematical models are proposed: one for the relationship between cancer and COVID19 and one for the relationship between heart disease, diabetes, and COVID19. This study aims to demonstrate how doctor controls are important for the future of human beings and how COVID19 will negatively affect these doctor visits. In this regard, two mathematical models are constructed. In
In this study, compartmental mathematical models are constructed. For the analysis of models, invariance, basic reproduction numbers, and equilibrium point properties are obtained and proved. Furthermore, for the effectiveness of parameters, sensitivity analysis is applied. All the data used in this paper for both models are gathered from the references [
In this section, the first model of the paper is proposed, and the entity of the solution is demonstrated. The model is constructed with the help of ordinary differential equations (ODEs) to obtain the change in compartments at time t. Then, an analysis of the model is given.
The whole population,
In
Variables  Descriptions 

Susceptible individuals  
Cancer patients 
Parameters  Descriptions 

Recruitment rate  
Transmission rate of hereditary  
Rate of obese individuals with cancer  
Rate of smokers with cancer  
Recovery rate  
Negative effect of COVID19  
Diseasecaused death rate  
Natural death rate 
for some arbitrary constant
For the constructed model, two equilibrium points, diseasefree and endemic equilibrium points, are evaluated. At the diseasefree equilibrium point, denoted by
It is obvious that
The endemic equilibrium point, denoted by
where
for
and
On the other hand, a real solution of the quadratic equation that depends on
This inequality always holds since the value of the natural death rate is very small.
The above function is always positive and at the point
Since
It is clear that
where
Thus,
In mathematical epidemiology, deterministic models of diseases rely significantly on data fitting to verify that their predictions are in line with observed data. The capacity to predict the spread of disease is enhanced since it simplifies the estimation of model parameters like transmission and recovery rates. By contrasting the model with the data, researchers can learn more about illness trends, treatment outcomes, and discrepancies and undertake whatif analyses. If policymakers had more faith in the model’s projections, they could make more educated choices. Improving future model development is another benefit of expanding the scientific knowledge base.
The least squares method has been extensively used in a wide variety of fields, from epidemiology to finance, to estimate parameters in mathematical models. When developing a deterministic model for infectious diseases, we first start with a set of differential equations that describe the dynamics of the disease. These equations may contain imprecise values for parameters like the rate of transmission or the rate of recovery. Model predictions produced with arbitrary settings for these parameters will not match the observed data. Finding these parameters’ values that yield predictions as close to the data as possible is the goal. To strike this equilibrium, the least squares approach minimizes the squared differences (also known as “residuals”) between the observed and expected values. Once the parameter values have been obtained, the squared deviations between the model’s predictions and the data can be easily calculated. Finding parameter values that minimize this sum is desirable since it indicates that the model’s predictions are close to the data. The model’s parameters are considered to be “fit” to the data once this constraint minimization is complete. With these modified parameters, the model should more faithfully capture the dynamics of the infectious disease’s transmission and impact as observed in the real world.
In epidemiology, fitting parameters to models using the ODE system in
The fitted parameters are obtained as follows:
The rest of the parameters are taken to be fixed and given to be
It may further be seen that the statistical measures (minimum, first, second, and third quartile (Q1, Q2, Q3), arithmetic mean, maximum, and standard deviation) computed in
Summary  Min.  Q1  Q2  Q3  Mean  Max  SD 

Real  2.65 * 10^(2)  2.87 * 10^(2)  3.43 * 10^(2)  4.18 * 10^(2)  3.74 * 10^(2)  4.94 * 10^(2)  8.33 * 10^(1) 
Observed  2.58 * 10^(2)  2.97 * 10^(2)  3.57 * 10^(2)  4.35 * 10^(2)  3.74 * 10^(2)  5.15 * 10^(2)  8.21 * 10^(1) 
In this section, the model is proposed with proof of the existence of the solution. Afterwards, analyses of equilibrium points are given.
The population, which is stated by
An explanation of the variables and parameters is given in
Variables  Descriptions 

Susceptible individuals  
Heart disease patients  
Diabetes patients 
Parameters  Descriptions 

Recruitment rate  
Rate of smokers who are heart patients  
Rate of obese individuals who are heart patients  
Rate of obese individuals who have diabetes  
Transmission rate of hereditary  
Negative effect of COVID19  
Survival rate of diseases  
Natural death rate  
Heartdiseasecaused death rates  
Diabetescaused death rates  
Transmission rate from 

Transmission rate from 
From the above equality, it is obvious that
In the proposed model, there are two equilibrium points: the diseasefree equilibrium point, denoted by
Here,
The endemic equilibrium point, denoted by
for
and so
Here, the constructed function
Similarly, if
Hence,
The constructed function
So,
According to the statistics proposed in [
Sensitivity analysis is a method that can be applied to the parameters of any mathematical model to identify the effect of the parameters on the compartments. This analysis aims to demonstrate how a small change in parameters can affect whether a disease exists or dies out [
In this part, a sensitivity analysis is implemented to the parameters of the first model.
In this part, a sensitivity analysis is implemented to the parameters of the second model.
In
The effect of the obesity parameter,
The main purpose of this study was to demonstrate how COVID19 will affect the future of chronic diseases such as cancer, heart disease, and diabetes. In this regard, two mathematical models were proposed and proved with the required theorems. The first model consists of cancerdiagnosed and susceptible individuals, while in the second model, heart disease patients, diabetic patients, and susceptible individuals are included. The reason for the two separate models is the unrelated connection of cancer with heart disease and diabetes.
In the analysis of the first model, diseasefree equilibrium,
In the same manner, the analysis of the second model demonstrated two existing equilibrium points for this model: the diseasefree equilibrium point,
In
In [
As a result of the figures from the models, it is concluded that obesity is an effective parameter for the studied diseases and an increase in it will affect patients negatively. Smoking affects cancer and heart disease patients badly in the case of utilization. Heredity is a significant parameter for patients with diabetes and heart disease. Hence, people with a family history of these diseases should ensure that they attend their doctor visits. In addition, there is a strong relationship that cannot be ignored between diabetes and heart disease patients. As maintained in
On the other hand, both of the models indicated that the most dangerous parameter for the disease is c, (a negative effect of COVID19), which is a result of the COVID19 pandemic. In conclusion, the results showed that being aware of COVID19 and its results may lead to a substantial decrease in deaths due to cancer, heart disease, and diabetes. That, combined with frequent doctor visits, could lead to the earlier diagnosis and treatment of these diseases.
This paper is prepared to emphasize the impact of COVID19 on other serious diseases. The main purpose is to show that more epidemics and even pandemics may occur in the future in the case of insufficient control strategies. The study revealed that one of the reasons for this is to avoid doctor visits and regular checks because of the infectiousness of COVID19. The presented study has a significant role in health sciences by being one of the strong models that discuss the effect of COVID19 pandemic with different and serious perspectives.
The results of the sensitivity analysis should be utilized by healthcare systems and policymakers to develop control strategies to achieve better public health. Because obesity is linked to numerous health problems, tackling the issue is of the utmost significance. Public campaigns highlighting the dangers of obesity for one’s health should be launched immediately. In addition, funding smoking cessation programs is essential because of the harm that smoking causes to people with cancer and cardiovascular diseases. These campaigns may include anything from a public information campaign to the distribution of free or lowcost cessation aid and community resources. Genetic counselling can be extremely helpful for people who have a history of diabetes or cardiovascular disease in their families. Individuals can learn more about the hazards they face and receive direction on how to mitigate those dangers during these sessions. Furthermore, given the welldocumented link between diabetes and cardiovascular disease, timely health checks are crucial. Patient results can be vastly improved by combining these checks with a holistic healthcare approach incorporating multidisciplinary teams. Cancer, heart disease, and diabetes are only some of the diseases whose rates and consequences have been significantly affected by the COVID19 pandemic and its aftermath. Therefore, efforts to inform the public about the longterm consequences of the virus’s spread are crucial. The transmission of the virus and the ensuing health consequences can be reduced by ensuring universal vaccination and the use of preventative measures. At the same time, there is a critical need for more indepth studies to understand the entire extent of the virus’s potential health effects. This knowledge is essential for the development of future public health treatments with greater precision. Individuals can be better prepared for disease treatment and prevention with the use of an integrated patient education framework that includes information on disease risks, symptom awareness, and the benefits of early diagnosis. Disease transmission that relies on memory qualities may also be better described by mathematical modeling with fractional derivatives [
As authors, we would like to thank the members of Mathematics Research Center of Near East University.
The authors received no specific funding for this study.
Study conception and design: F.N.E., N.G., E.H.; data collection: S.Q., K.H., A.S.; analysis and interpretation of results: S.Q., N.G., K.H., E.H.; draft manuscript preparation: F.N.E., S.Q., N.G., K.H., A.S. All authors reviewed the results and approved the final version of the manuscript.
All data generated or analyzed during this study are included in this article.
The authors declare that they have no conflicts of interest to report regarding the present study.