Considering Interim Interventions to Control COVID-19 Associated Morbidity and Mortality-Perspectives

Abstract

Aims and objectives: The pandemic of COVID-19 is evolving worldwide, and it is associated with high mortality and morbidity. There is a growing need to discuss the elements of a coordinated strategy to control the spread and mitigate the severity of COVID-19. H1N1 and Streptococcus pneumonia vaccines are available. The current analysis was performed to analyze the severity of COVID-19 and influenza (H1N1) vaccination in adults ≥ 65. Also, to correlate the lower respiratory tract infections (LRIs), and influenza attributable to the lower respiratory tract infections' incidence with Covid-19 mortality. Evolutionarily influenza is close in resemblance to SARS-CoV-2 viruses and shares some common epitopes and mechanisms. Methods: Recent influenza vaccination data of 34 countries from OECD and other publications were correlated with COVID-19 mortality from worldometer data. LRIs attributable to influenza and streptococcus pneumonia were correlated with COVID-19 mortality. Specifically, influenza-attributable LRI incidence data of various countries (n = 182) was correlated with COVID-19 death by linear regression and receiver operating characteristic (ROC) curve analyzes. In a logistic regression model, population density and influenza LRI incidence were correlated with COVID-19 mortality. Results: There is a correlation between COVID-19-related mortality, morbidity, and case incidence and the status of influenza vaccination, which appears protective. The tendency of correlation is increasingly highlighted as the pandemic is evolving. In countries where influenza immunization is less common, there is a correlation between LRIs and influenza attributable to LRI incidence and COVID-19 severity, which is beneficial. ROC curve showed an area under the curve of 0.86 (CI 0.78 to 0.944, P < 0.0001) to predict COVID-19 mortality >150/million and a decreasing trend of influenza LRI episodes. To predict COVID-19 mortality of >200/million population, the odds ratio for influenza incidence/100,000 was -1.86 (CI -2.75 to -0.96, P < 0.0001). To predict the parameter Covid-19 mortality/influenza LRI episodes*1000>1000, the influenza parameter had an odd's ratio of -3.83 (CI -5.98 to -1.67), and an AUC of 0.94. Conclusion: Influenza (H1N1) vaccination can be used as an interim measure to mitigate the severity of COVID-19 in the general population. In appropriate high-risk circumstances, Streptococcus pneumonia vaccination would also be an adjunct strategy, especially in countries with a lower incidence of LRIs.

Keywords: COVID-19 Mortality and morbidity; SARS-CoV-2; Streptococcus pneumonia vaccine; influenza lower respiratory tract infections; influenza vaccination; lower respiratory tract infections.

Copyright © 2020 Arokiaraj.

Figures

Figure 1

Correlation between influenza vaccination percentage (age ≥ 65 years), and (cases per million population) vaccination percentage, (A) May 8, 2020, and (B) May 18, 2020. (C) Shows the case per-million value adjusted to tests performed (May 18, 2020) and (D,E) the data of June 1 2020. (F) Shows linear and logarithmic trendlines (July 4, 2020).

Figure 2

Correlation between influenza vaccination percentage (age ≥ 65 years) and mortality per million population/vaccination percentage [(A) April 10, 2020, (B) April 15, 2020, lower panel April 20, 2020, (C) April 20, 2020, (D) April 25, 2020, (E) May 1, 2020, (F) May 8, 2020, (G) May 31, 2020, and (H) July 4, 2020].

Figure 3

Correlation between influenza vaccination percentage vs. [(deaths/million)/cases per million]/vaccination [(A) April 14, 2020, (B) April 17, 2020, (C) April 20, 2020, (D) April 25, 2020, (E) May 1, 2020, (F) May 8, 2020, and (G) May 18, 2020, (H) May 31, 2020, and (I) July 4, 2020].

Figure 4

Correlation between vaccination and death per million/cases per million adjusted to population [(A) May 18, 2020 and (D) May 31, 2020] and tests performed [(B) and (C), May 31, 2020].

Figure 5

Correlation between influenza vaccination percentage (age ≥ 65 years), vs. [(Deaths/million)/Cases per million]/Vaccination after correction for tests performed per million [(A), April 10, 2020, (B) April 20, 2020, (C) April 25, 2020, (D) May 1, 2020, (E) May 8, 2020, (F) May 18, 2020, and (G) May 31, 2020].

Figure 6

Mortality/Cases adjusted to tests by denominator (A,C,E). Mortality/cases values adjusted to population in (B,D,F).

Figure 7

Correlation between influenza vaccination percentage and (critical number/million population)/vaccination percentage [(A) April 10, 2020, (B) April 18, 2020, (C) May 1, 2020, (D) May 7, 2020, and (E) May 18, 2020].

Figure 8

Correlation between influenza vaccination percentage, and [(critical number/million population)/(total cases per million)]/vaccination percentage [(A) April 26, 2020, (B) May 1, 2020, (C) May 8, 2020, (D) May 18, 2020 and (E) July 5, 2020].

Figure 9

Correlation of influenza incidence/100,000 population and COVID-19 mortality/million population (n = 182, June 21, 2020).

Figure 10

Receiver Operating Characteristics (ROC) curve analysis of the influenza incidence and the mortality rates of COVID-19 (cut-off mortality rate at 50/million, area under the curve-AUC 0.72; (A) 75/million, AUC 0.78, (B) 100/million, AUC 0.81, (C), 150/million, AUC 0.86, (D) 200/million, AUC 0.85, and (E) Data June 21, 2020).

Figure 11

Correlation of Influenza LRI's/100,000 (x) vs. COVID-19 Mortality in various countries (y) (July 8, 2020 n = 182).

Figure 12

Correlation of Influenza LRI's/100,000 (x) vs. Variable (y), COVID-19 Mortality/Influenza LRI, July 8, 2020 (n = 182).

Figure 13

ROC curve statistics of Influenza and COVID-19 mortality (A–D), and ROC curve of influenza LRI vs. parameter (COVID-19 Mortality/Influenza LRI) (July 8, 2020) for (COVID-19 mortality/influenza*1,000) parameter >500 and > 1,000 [(E,F), respectively].