The notable global heterogeneity in the distribution of COVID-19 cases and the association with pre-existing parasitic diseases

Background The coronavirus Disease 2019 (COVID-19) is a respiratory disease that has caused extensive ravages worldwide since being declared a pandemic by the World Health Organization (WHO). Unlike initially predicted by WHO, the incidence and severity of COVID-19 appeared milder in many Low-to-Middle-Income Countries (LMIC). To explain this noticeable disparity between countries, many hypotheses, including socio-demographic and geographic factors, have been put forward. This study aimed to estimate the possible association of parasitic diseases with COVID-19 as either protective agents or potential risk factors. Methods/Principal findings A country-level ecological study using publicly available data of countries was conducted. We conceptualized the true number of COVID-19 infections based on a function of test positivity rate (TPR) and employed linear regression analysis to assess the association between the outcome and parasitic diseases. We considered demographic, socioeconomic, and geographic confounders previously suggested. A notable heterogeneity was observed across WHO regions. The countries in Africa (AFRO) showed the lowest rates of COVID-19 incidence, and the countries in the Americas (AMRO) presented the highest. The multivariable model results were computed using 165 countries, excluding missing values. In the models analyzed, lower COVID-19 incidence rates were consistently observed in malaria-endemic countries, even accounting for potential confounding variables, Gross Domestic Product (GDP) per capita, the population aged 65 and above, and differences in the duration of COVID-19. However, the other parasitic diseases were not significantly associated with the spread of the pandemic. Conclusions/Significance This study suggests that malaria prevalence is an essential factor that explains variability in the observed incidence of COVID-19 cases at the national level. Potential associations of COVID-19 with schistosomiasis and soil-transmitted helminthiases (STHs) are worthy of further investigation but appeared unlikely, based on this analysis, to be critical factors of the variability in COVID-19 epidemic trends. The quality of publicly accessible data and its ecological design constrained our research, with fundamental disparities in monitoring and testing capabilities between countries. Research at the subnational or individual level should be conducted to explore hypotheses further.

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• All epidemiological and socio-economic data are available from the Global Health Observatory (https://www.who.int/data/gho/data/indicators) and World Development CoV-2. Therefore, we performed a nation-level ecological analysis to describe trends in primary SARS-55 3 CoV-2 outcomes by country and investigate potential correlations between these outcomes and pre-56 existing parasitic diseases. Our results suggest that malaria prevalence is a crucial factor to explain 57 variation in COVID-19 incidence at the national scale, with a substantial relationship persisting even 58 when putative confounders were adjusted. While we note that causal inference cannot be proved owing 59 to insufficient data and hidden confounders, this will aid in generating new hypotheses and identifying 60 intervention strategies to reduce NTDs and malaria in the context of the coronavirus pandemic.    Human-infecting parasites have coevolved with their hosts during evolutionary processes, with 100 persistent immune system modulation through molecular and physiological mechanisms [15][16][17][18]. potentially support viral infection [21,22], affect the efficiency of vaccines [23], or protect the human 109 host from detrimental consequences of COVID-19 by reducing inflammatory reactions [15,24,25].

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Five species of the genus Plasmodium are known to cause malaria in humans (Table 1). Clinical  This study aimed to identify the potential influence of pre-existing parasitic infections on COVID-122 19 morbidity. The study also considered demographic, socioeconomic, and geographic variables 123 documented in previous epidemiological studies. We conducted an ecological study using country-level 124 data to describe patterns in COVID-19 incidence rates and explore possible associations of parasitic 125 diseases with COVID-19 while adjusting the effect of confounding factors. Our results will be essential 126 for developing prevention and control strategies to reduce NTDs and malaria in the contexts of the 127 coronavirus pandemic.  Multivariable linear regression 175 We applied univariable and multivariable linear regression analysis to investigate the associations. All  predictors to the level at which the adjusted R 2 ( 2 ) is not higher above the 2 of the complete model.

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The selection step was carried out by referring to the study that documented the importance of Pacific (WPR), and Africa (AFR) (Fig 1). This trend has been consistent since the onset of the disease.

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The distribution of parasitic diseases was also heterogeneous across regions (Fig 2). The countries     After screening significant variables using univariable analysis, variables with pairwise correlation 259 coefficients between -0.7 and 0.7 were comprised for further multivariable linear regression (S1 Fig).

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The saturated model had the least value of AIC and BIC (S1 Table). Since the AIC and BIC significantly COVID-19 incidence rates, the outcome variable monotonically decreased as the predictor increased (β 274 coefficient = -0.02; 95 % CI of -0.04 to -0.01; p < 0.01) (Fig 3).  We performed an ecological study to reveal crucial factors that may explain the worldwide disparity of 301 SARS-CoV-2 epidemics, focusing on the significance of exposure to endemic parasitic infections. We 302 identified that malaria prevalence is an essential factor explaining variation in observed case incidence 303 of COVID-19 at the national level, with a significant association retaining even when possible 304 confounders were adjusted. Potential associations of schistosomiasis and STH with SARS-CoV-2 305 infection appeared unlikely, from this study, to be critical factors in the heterogeneity in SARS-CoV-2 306 epidemic trends. Overall, while we emphasize that causal inference cannot be concluded due to 307 incomplete data and hidden confounders, we discovered that the prevalence of malaria has a consistent 308 association that indicates its impact on COVID-19 epidemics, indicating that further research is needed.

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The malaria prevalence was the only measure of parasitic infection that consistently showed a strong 310 negative association with COVID-19 incidence rates. One plausible biological mechanism might be the 311 variable distribution of the polymorphisms associated with angiotensin-converting enzyme 2 (ACE2).

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A type I transmembrane amino-peptidase, ACE2, is anchored at the surface of cells of the heart, kidneys,    This study aimed to assess the role of parasitic diseases in explaining the spread of COVID-19, to 407 understand why the disease is progressing at a slower pace in LMICs. Malaria prevalence appears to be 408 an essential element associated with the epidemics of SARS-CoV-2, even after potential confounding 409 factors were adjusted. Our analyses suggest significantly lower COVID-19 incidence rates in malaria- We have no acknowledgment related to this work.

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Conflicts of interest 424 We have no conflict of interest related to this work.