Socioeconomic achievement of WASH (access to safe water, sanitation and hygiene) services are being acknowledged as anticipatory actors, indispensable in safeguarding health during this Covid-19 pandemic. However, on a global scale, it is currently not clear whether deprivation or non-obtainability of which of the various WASH services are closely related to Covid-19 dynamics and up to which degree. We have analysed data (March -June 2020) related to five Covid-19 indicators for most of the countries in the world with indicators of safe water, sanitation and hygiene to understand this. We have found a strong positive correlation between lesser effects of Covid-19 and better access to safe water, sanitation as well as hygiene throughout this time for most of the indicators. However, some indicators show the opposite nature of the relationship, for which we have given probable explanation accordingly. The hypothesis of an inversely proportional association between Covid-19 and poor WASH facilities on a global scale is confirmed in this study. We propose that this study should be perceived as an expanded comprehensive view on the complexities of WaSH-Covid19 interrelationships, which could help to shape an agenda for research into some unanswered questions.
Keywords: water; hygiene; sanitation; global; socioeconomy; covid-19;
Severe acute respiratory syndrome-coronavirus (SARS-CoV-2), emerged in China in December 2019, is causing an outbreak of respiratory disease (COVID-19 disease by WHO).
The new SARS-CoV-2 virus is known to spread by person-to-person contact (through respiratory droplets over a short distance) or via faecal-oral routes (Heller et al. 2020) .
Studies have shown that coronaviruses exist and can maintain their viability in sewage and hospital wastewater, originating from the faecal discharge of infected patients. Concerns have been raised over inequity in access to various prevention and control measures for slum dwellers, refugees etc. (Singh et al. 2020) , even though Tedros Adhanom Ghebreyesus (DG, WHO) has urged a "whole-of-government, whole-of-society approach" for Covid-19 (Lau et al. 2020 ). According to Lau et al. (2020) , one of the important reasons for this might be limited access to safe water and sanitation. Connection of control of covid-19 and global access to handwashing facilities has also been established for low-income countries (Brauer et al. 2020) . The insecurity of water may also act as a deterrent for covid-19 mitigation, especially in developing areas (Stoler et al., 2020) . Odith et al. (2020) have also emphasized probable induction of Covid-19 transmission rate from the shortfall in water and sanitation, using Nigeria as a case study. Amankwaa & Fischer (2020) and Jiwani & Antiporta (2020) have also emphasised on the effects of poor WASH services on Covid-19 fatalities in sub-Saharan African countries. Caruso & Freeman (2020) 
We have collected data of water, sanitation and hygiene from WHO/UNICEF JMP (2020), WDI (2020). We have used the data from the latest available year in this work. We have collected data of Covid-19 from for 4 dates (30 th of March to 30 th June, 2020) from JHU CCSE (2020). We have used data of indicators as: 10 for water, 11 for sanitation, 3 for hygiene and 5 for Covid-19. List of indicators are given in table. More details about sub indicators are provided in supplementary file.
The statistical analysis has been conducted using R 3.6.2, (R Core Team. 2019) and 'Hmisc' We have used income groups to understand any fixed trend with countries belonging to a specific income bracket and possible variations along 4 different income groups (viz. high, upper-middle, lower-middle and low income, as per World Bank Income groups, 2020). We have also divided countries into 7 geographic zones to understand possible intra-region similarities and inter-region differences. These regions are -(1) East Asia & Pacific, (2)
North America, (6) South Asia and (7) sub-Saharan Africa. We have selected 2 indicators of highest correlation values of each of WASH (water related -2, sanitation related -2 and hygiene related -2) with a recovery rate of Covid-19 to understand these 2 aspects. 
Most of the indicators that represent good socioeconomic condition related to water, likebasic drinking water, piped services, on-premises services, available when needed services, safely managed drinking water services etc. are strongly positively related to total confirmed, recovered and death. They show less strongly positive correlation with recovery rate and even lesser with case fatality rate. Another group of indicators that represent not so good socioeconomic condition related to water, like -non-piped services, unimproved water services, limited water services etc. have a strongly negative correlation to total confirmed, recovered and death. They show a less strong negative correlation with recovery rate and even lesser with case fatality rate.
However, if we delve deeper into each indicator, the see some apparent conflict in results, for all 4 dates (i.e. total span of the study, 4 months), especially concerning total confirmed (TC) and total death (TD). Some of the indicators that represent good (BDWS) or better (IWS, IWS -P, IWS -AN, IWS -AP) conditions of safe and better access to water are positively related to total confirmed and total death. Also, some of the indicators that represent poor (UWS, LWS) of safe and better access to water are negatively related to total death. 
Most of the indicators that represent good socioeconomic condition related to sanitation, like -basic sanitation services, improved sanitation facilities, sewer connection facilities, wastewater treated facilities, septic tank facilities etc. are strongly positively related to total confirmed, recovered and death. They show a less strong positive correlation with recovery rate and even lesser with case fatality rate. Another group of indicators that represent not so 
Basic handwashing facility has a strong positive correlation to total confirmed, recovered, death and recovery rate. However, it has a less strong positive correlation with the case fatality rate. Another group of hygiene indicator that represents not so good socioeconomic condition, like -limited and no handwashing facilities have a strong negative correlation with total confirmed, recovered, death and recovery rate. These have a less strong negative correlation with the case fatality rate.
As the 2 previous components of WASH (i.e. water & sanitation), we also see some conflicting results. Indicators that represent good (BHF) conditions of safe and better access to hygiene facilities are positively correlated to total confirmed (TC) and total death (TD). 
Firstly, the case fatality rate and recovery rate show a lesser degree of correlation with WASH. Values remain closer to zero. However, as time progresses, they show a stronger correlation with socioeconomic indicators of WASH. Among the 5 indicators of Covid-19, total confirmed, recovered and death always show a significant level of correlation for all the dates. Recovery rate has shown a similar nature of significant correlation to all sectors, excluding -hygiene throughout all 4 dates. CFR has gradually become more significant as time passes in overall, water and sanitation sectors. However, for hygiene, there are no significant correlations with CFR for all dates. Dendrogram shows clear distinction between 2 groups of indicators related to WASH, -those that represent good and better conditions of WASH achievement and those that represent poor conditions of WASH. This grouping remains fixed for all 4 dates. However, the same cannot be said in case of indicators. Case fatality rate and recovery rate belong to one group and other three indicators (TC, TR and TD) belong to another group, for first two dates. As time progresses (i.e. in last 2 dates), though CFR maintains similar distance, recovery rate has become more closely associated with the other group of indicators (Fig 2) . This can also be seen in 
We have plotted WASH indicators with Covid-19 (recovery rate) of countries following income groups (of World Bank) and also for diving countries into 7 geographic regions. High and upper-middle countries, with better WASH conditions, are showing better recovery rate of Covid-19 (see supplementary file). However, as the income range decreases (i.e. uppermiddle to lower-middle to low-income groups), Covid-19 performance is not adhering to any specific type. There is no group or clustering for any of these income groups. Similarly, for 7 geographic regions, we can see a high frequency of overlaps i.e. not forming any regional groups of Covid-19 performance pattern. We have also used urban and rural area WASH performance (sub-indicators, see supplementary file). For 85-90% of indicators, urban are rural are proving to be as significant as national (i.e. overall) WASH performance, for Covid-19.
This study depicts clearly that there is indeed a strong relationship between WASH and Covid-19, globally. If we consider water and sanitation ladder, the results also show a directly proportional relationship between various WASH indicators and Covid-19. This means better socioeconomic conditions of WASH in related to better performance against Covid-19 in countries of the world. However, from the results, we can also see that basic or improved access to WASH facilities does not always correspond to better resilience against Covid-19. The trends also show a string of changes happening over time. We can infer that these are possibly due to changing weightage of effects of other factors (other than WASH) as a contributor (for or against). We think that these other contributing factors might beglobalization, degree of connectedness, level of trade association, population density, access of healthcare facilities etc. Countries that have better WASH conditions are also the countries higher position in these factors. Though most of the high-income countries are showing similar nature of the relationship between WASH and Covid-19. Also, as we move down through the income groups, there strikingly dissimilar response in this context. We think this might be due to the variable nature of socioeconomy. Countries belonging to the same income group might have different nature of natural resource consumption and trade. Also, once Covid-19 started, strategies are taken and policies implemented are very different in these countries. Hence, responses of covid-19 are also varying. It is also very little to no similarity of Covid-19 response about WASH among countries that are located in the same geographic region. This can also be explained similarly. Various countries, of same geographic location, have implemented different strategy and policy related to Covid-19, hence are not behaving similarly as a group. These results indicate that geography and economy-wise countries might have been in a similar nature and degree of relationship between WASH and Covid-19, but it has been changing with time. The WASH data is annual, also the latest comprehensive update was from 2017. If these were at least seasonal and recent (like from 2019), the nature of the relationship of WASH and Covid-19 could have been clearer. Condition of WASH is one of the important factors related to Covid-19.
However, many other socioeconomic factors might have emerged to be gradually becoming more influential than the WASH situation. In this presence of multiple factors, there also a possibility of interaction among the factors and the resulting force is reflected among the socio-economic behaviour of Covid-19.
From this work, it is clear that Covid-19 pandemic is a complex phenomenon. Various factors have influenced where and when this viral infestation has started, how it is changing and also when and how it is being controlled, at least up to a certain degree. The pre-existing social condition of WASH might be a major driver of Covid-19 spread for many countries, but not all. Economically, a similar group of countries have also not shown similar response to covid-19 spread, related to WASH condition. Likewise, for case fatality and recovery too, WASH conditions are the major contributor for some of the countries, but not all. This gives us an idea about many things, as -(a) not-so-linear relationship between Covid-19 and WASH, (2) heterogenous nature of socioeconomic achievement levels of WASH with the onset and spared of covid-19, (c) possible effects of many others factors as a driving force of covid-19 in some countries etc. All of these indicate towards a nexus of interacting factors, including WASH, that has shaped the onset, spread and mitigation of Covid-19 pandemic in countries of the world. Hence, more research is needed in this Covid-19 -economy -society interaction to understand this in a more comprehensive way towards actionable decision making.