A Non-Linear Biostatistical Graphical Modeling of Preventive Actions and Healthcare Factors in Controlling COVID-19 Pandemic

Abstract

Background: With the insurgence of the COVID-19 pandemic, many people died in the past several months, and the situation is ongoing with increasing health, social, and economic panic and vulnerability. As most of the countries relying on different preventive actions to control the outcomes of COVID-19, it is necessary to boost the knowledge about the effectiveness of such actions so that the policymakers take their country-based appropriate actions. This study generates evidence of taking the most impactful actions to combat COVID-19.

Objective: In order to generate community-based scientific evidence, this study analyzed the outcome of COVID-19 in response to different control measures, healthcare facilities, life expectancy, and prevalent diseases.

Methods: It used more than a hundred countries' data collected from different databases. We performed a comparative graphical analysis with non-linear correlation estimation using R.

Results: The reduction of COVID-19 cases is strongly correlated with the earliness of preventive initiation. The apathy of taking nationwide immediate precaution measures has been identified as one of the critical reasons to make the circumstances worse. There is significant non-linear relationship between COVID-19 case fatality and number of physicians (NCC = 0.22; p-value ≤ 0.001), nurses and midwives (NCC = 0.17; p-value ≤ 0.001), hospital beds (NCC = 0.20; p-value ≤ 0.001), life expectancy of both sexes (NCC = 0.22; p-value ≤ 0.001), life expectancy of female (NCC = 0.27; p-value ≤ 0.001), and life expectancy of male (NCC = 0.19; p-value ≤ 0.001). COVID-19 deaths were found to be reduced with increased medical personnel and hospital beds. Interestingly, no association between the comorbidities and severity of COVID-19 was found excluding asthma, cancer, Alzheimer's, and smoking.

Conclusions: Enhancing healthcare facilities and early imposing the control measures could be valuable to prevent the COVID-19 pandemic. No association between COVID-19 and other comorbidities warranted further investigation at the pathobiological level.

Keywords: COVID-19 pandemic; case fatality rate; life expectancy; lockdown; quarantine; social distance.

Figure 1

Comparative analysis of the healthcare measures undertaken by the government for top 20 high (a) and top 20 low (b) case-death countries. The considered measures can be categorized as public health measures (screening at airport and land borders, isolation and quarantine policies, strengthening the public health system, awareness campaigns, psychological, and medical social work), social distancing (closure of public services, restriction on public gatherings, closure of schools), governance and socio-economic measures (declaration of state of emergency, emergency administrative structures, limiting import and export system, military deployment), movement restrictions (restriction on visa and travelling, border shutdowns, curfew), and lockdown (partial lockdown, full lockdown).

Figure 2

Relationship among COVID-19 case fatality, healthcare resources, and life expectancy where (a–c) represent the association between COVID-19 case fatality and the number of physicians, nurses and midwives, and hospital beds, respectively while (d) indicates the relationship between COVID-19 case fatality and life expectancy by gender for all 154 countries where COVID-19 infection was identified on 20 April 2020; *** represent significant at 0.1%. Note: The country list corresponding to the index in the horizontal axis of all figures are presented in Table S1.

Figure 3

Association between COVID-19 cases and case fatality and some selected comorbidities death rates for all 163 countries where COVID-19 infection was identified on 20 April 2020. Note: Comorbidity variables considered here are asthma, diabetics, breast cancer (BC), AIDS, influenza & pneumonia (IP), tuberculosis, stroke, smoke, coronary heart disease (CHD), and hypertension. Each pair of graphs from left to right with a selected comorbidity variable are showing COVID-19 confirmed cases and deaths respectively. The country list corresponding to the index in the horizontal axis of all figures are presented in Table S2.

Figure 4

Association between COVID-19 cases and case fatality and some selected comorbidities prevalence rates where COVID-19 infection was identified on 20 April 2020. Note: Comorbidity variables considered here are asthma (170 countries), diabetics (180 countries), breast cancer (BC) (170 countries), AIDS (127 countries), liver cancer (LC) (170 countries), tuberculosis (170 countries), stomach cancer (SC) (170 countries), and smoking (168 countries). Each pair of graphs from left to right with a selected comorbidity variable are showing COVID-19 confirmed cases and deaths respectively. The country list corresponding to the index in the horizontal axis of all figures are presented in Table S3.