The new coronavirus has become a pandemic and poses a serious threat to the human population. It has an infection rate that has increased exponentially. Millions of people have died worldwide due to the COVID-19 epidemic. Infection rates have increased rapidly since the lockdown was lifted in many countries. In fact, virus infections have reached their peak in December 2021. Many environmental and biological factors could contribute to rapid spread, including metabolic parameters and temperature. Studies have shown a link between infection and transmission of viruses and air temperature and humidity. This was demonstrated, for instance, for the flu virus. 15. According to literature, temperature may be a factor in the transmission of coronaviruses like SARS-CoV.16MERS-CoV17Because of (i), increased virus half-life when temperatures are lower, (ii), greater stability in the nasal passages if the epithelial surfaces are cold, and (iii), greater stability at lower humidity levels than in intermediate humidity.15,16,17. We conducted a study to determine if there is a correlation between environmental temperature and COVID-19 case. We discovered that the countries with more COVID-19-related cases were most often located north of Wuhan in China, where the pandemic broke out in December 2019. We therefore performed a detailed country-wise statistical analysis that revealed a significant negative correlation between COVID-19 case and MAET of a country.5. However, this initial finding was limited only to COVID-19 data from March 2020 and April 2020. There are a few studies that are more consistent with our findings regarding temperature and SARS CoV-2 caseloads.18Others have not found a correlation between temperature, infection rate, or any other variables.13. We have verified the relationship between MAET (cold, inflammation, and disease) and COVID (19 cases per million) from March 2020 to July 2021 in a country. Both Spearmans and Pearson Univariate Analysis showed a negative correlation between COVID-19 cases and temperature (Table 1). The statistical analysis also showed a stronger negative correlation between temperature and COVID-19 cases for the winter months (November through March). This indicates that the warmer months were more susceptible to SARS-CoV-2 infection than the colder ones. The simple trajectory curves and the box plot (Fig. 2) also show a higher incidence of COVID-19 infection than in the first months. We also examined the geographic locations of countries with higher rates of infection to confirm that lower temperatures are a factor in the incidence of disease. Most of these countries were located above 23.5ON latitude and/or towards poles suggest that cold temperatures could affect the SARS/CoV-2 transmission (Fig.1A).
We did not restrict our study to the environmental temperature as there could be other causes for the increase in coronavirus infections. Patients who have already been diagnosed with the virus are at greater risk.19. We therefore refined our research by taking into account additional metabolic parameters such as high-cholesterol and BMI, obesity, and the environment temperature of a country in order to influence the SARS-CoV-2 casesload. Multiple studies have shown that cholesterol plays a significant role in the virulence of other respiratory virus, such as influenza. For example, cholesterol, which maintains the membrane structure, is critical to viral stability.20. Studies have shown that patients with high cholesterol levels are more susceptible to viral infection, eventually leading to severe diseases.21. Cholesterol-enriched lipid rafts may be able to accommodate the aggregation ACE2 receptors on cell membranes, thus increasing the binding of SARS-CoV-2 at the host cell surface22. Another study found that people with an apo (apo) E4 genotype are at greater risk of severe COVID19 infections. ACE2 and furin trafficking within host cells are promoted by higher cholesterol levels21After infection, patients with high cholesterol levels have their plasma levels decrease. In short, high levels of cholesterol in the host cell membrane, virus particles and human blood may increase the virus entry processing within the host cells.21,23. Our data showed that 23.5 was the highest geographic location.ON latitude and towards poles had a higher prevalence for high average total cholesterol, often overlapping areas with the highest COVID-19 (Fig.1B). Univariate analysis also revealed a significant positive correlation between COVID-19 total cases per thousand and average total cholesterol (Table 1), suggesting that higher levels of cholesterol may increase the SARS-CoV-2 infection rate.
Obesity is a serious condition that is caused by our modern lifestyle. There is a link between obesity and serious viral infections, in addition to other health consequences.24. Studies have shown that obesity can lead to the spread of viral infections, such as Hepatitis C.25. Many studies have shown that overweight patients are more likely to require respiratory support than patients of normal weight.26. A cohort study found that obesity is a major factor in the severity and morbidity of SARSCoV2. Patients with a BMI 35 or higher had the greatest impact.27. In vitro experiments also showed that ACE2 (and TMPRSS2), two crucial entry components for SARS, were highly upregulated in the lungs of obese patients.28. We therefore explored the relationship between BMI, obesity and COVID-19 casesload. After identifying the geographical locations in which countries have higher rates of BMI and obesity, significant overlap was seen with those with high levels of COVID-19 cases (Fig.1C.D), just like with cholesterol levels. A statistical analysis also showed a positive correlation between BMI and the number of COVID-19 cases per capita (Table 1).
Based on the preliminary results of the univariate data analysis, we conclude that these metabolic factors, i.e. average total cholesterol and body mass index, influence the infectivity SARS-CoV-2 virus. These findings were verified using different statistical approaches. We tried to model the COVID-19 trajectory in terms of cases/millions using a latent growth curve model with time-variant, invariant variables. Multiphase GCM was used for investigating the impact of metabolic parameters on the escalation in COVID-19 cases. We evaluated various covariates, such as temperature, average cholesterol, and BMI, and tried to fit them in multiphase models with the COVID-19 case per million. We also examined the AIC and TLI values (Table 2). The model with the lowest AIC/BIC values and the highest TLC values was the best fitting model. This model was then ranked according to all statistical criteria. This metabolic parameter was also evaluated with the environmental temperature to determine if it had a greater impact on the infected rate. To determine the combination effect of all these factors (e.g., environmental temperature, average cholesterol and BMI with COVID-19 cases/million), we combined these parameters in the multiphase modeling and determined the AIC, BIC and TLI values. This model has the lowest AIC and BIC value and highest TLI value compared to other models. It can be concluded that Model-8 has outperformed all other models. These were the estimates that were taken from this model. Fig. 4 shows the structure plot for all countries. These findings suggest that patients with higher cholesterol, BMI and obesity are more likely to get infected, especially during winter months. SARS-CoV-2 infection is more likely in obese patients with high total cholesterol. This is the first attempt at modeling COVID-19 cases/million trajectory with the latent growth curve model in the presence of time-variant as well as invariant factors. Such a growth curve modeling approach could actually be used to track the spread of SARS/COV-2 infection over time in the presence of the considered variables. This can help to design policies against COVID-19.
Our study found a negative correlation in temperature and COVID-19 case numbers. However, this virus’s ability infect people may also depend upon their age, gender, ethnicity, prevalence of other diseases, social distancing practices, as well as the use of various preventive medicine. Our findings also focus on the effect of temperature on COVID-19 incidences. However, it is still unclear how indoor temperatures might impact infection rate. The study uses a holistic approach to understand the role of temperature in virus infection rates and also takes into account fluctuations in a single country. It is also difficult to speculate about the influence of temperature, obesity and cholesterol on these mutant strains’ infection rates due to the emergence SARS-CoV-2 mutations. However, most people are protected against reinfection for at the very least five months after an SARS-CoV-2-related infection.29The first case of COVID-19 Reinfection after Recovery has been identified in a Japanese Female30After that, reinfection became a real threat. A recent study found that people over 65 years old have a low level of protection against reinfection by COVID-19.31. Recent studies have also shown that higher antibody levels are associated with obesity and hyperlipidemia.32,33. All of these findings suggest that different metabolic factors may not only increase infectivity but also trigger reinfection. It is not clear how metabolic parameters such as obesity and cholesterol affect the incidences of reinfection. Despite this, the pattern of virus infection could change in the near-term due to improved treatment and our growing knowledge about the SARS/CoV-2 virus and its complications.
This study also suggests that people with metabolic disorders like obesity and high-cholesterol may be more vulnerable to SARS-CoV-2 infections in winter months, particularly if they live in colder environments. In response to cold environments and winter months, ACE-2 expression in host cells may increase and average total cholesterol levels may increase9,34,35. Multiple metabolic diseases such as diabetes, obesity, and high levels of LDL cholesterol have been associated with elevated ACE-2 levels.36. Furthermore, obesity may increase ACE-2 or TRMPSS2, as well as cellular cholesterol levels through an increase in SREBP1.37,38. It has been shown that low temperature plays a significant role in stabilizing RBDACE2 interfaces and triggering open conformations in the COVID-19 spike protein. This increases viral infectivity at lower temperatures.39. Numerous studies have also found multiple roles for cholesterol in increasing susceptibility to SARS/CoV-2 infection. Microdomains rich in cholesterol can be a good platform for interaction between ACE2 & Spike S-protein.7. Tang et. alThe role of cholesterol in increasing density of ACE2 receptors on host cells membranes was reported23. Reports using superresolution imaging also showed an increased SARS/CoV-2 entry in cells with high levels of blood serum cholesterol.40. Studies have shown that obesity plays a significant role in COVID-19 severity. Patients who are obese have a higher level of ACE-2 expression in their lung tissue. This suggests that excess adipose tissues may be a factor in the virus’s spread.41. Thus, a colder climate and obesity both increase ACE-2, host membrane cholesterol, which favors viral entry processing and increases virus infectivity.
These analyses do not consider other intrinsic factors, such as hypertension, heart disease, kidney disease, and cancer, or extrinsic variables like relative humidity, indoor temperature, and so on. Infectivity may be influenced by indoor temperature. Low relative humidity (RH), and cold temperatures can adversely affect the half-lives.15. Aerosolized SARS-2CoV-2 may be infective for up to 16 hours under optimum indoor meteorological conditions42. Indoors have a relative humidity of 40% which means that there are higher chances of SARS-CoV-2 infection. SARS-CoV-2 infections can be found in cool, dry, and well-ventilated indoor environments.43,44People stay inside during cold weather, which further enhances transmission45. The USA has policies that recommend indoor temperatures of 20 to 24C and RH of around 2060%.46. Dry weather, which is necessary to maintain indoor temperatures (20C) in winter months, may increase virus infectivity. This is because the virus may persist longer in lower humidity environments.47.
This is a brief summary of the conclusion. Multiphase growth curve models may be used as a way to illustrate the contribution of covariates to COVID-19. Infectivity may also be more likely in people with metabolic disorders such high cholesterol or obesity, who live in colder climates and are at higher risk in winter months. This study recommends that a national policy is developed to combat COVID-19 and its clinical outcomes in order to care for vulnerable individuals with these metabolic diseases. These factors are still being studied.
