key: cord-0850057-mowedl4n
authors: Kulkarni, Hemant; Khandait, Harshwardhan; Narlawar, Uday W.; Rathod, Pragati; Mamtani, Manju
title: Independent Association Of Meteorological Characteristics With Initial Spread Of Covid-19 In India
date: 2020-10-16
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.142801
sha: 3f214777c46fb266b0cb2129235471e0cfa4c8ae
doc_id: 850057
cord_uid: mowedl4n

Whether weather plays a part in the transmissibility of the novel Coronavirus Disease-19 (COVID-19) is still not established. We tested the hypothesis that meteorological factors (air temperature, relative humidity, air pressure, wind speed and rainfall) are independently associated with transmissibility of COVID-19 quantified using the basic reproduction rate (R0). We used publicly available datasets on daily COVID-19 case counts (total n = 108,308), three-hourly meteorological data and community mobility data over a three-month period. Estimated R0 varied between 1.15-1.28. Mean daily air temperature (inversely), wind speed (positively) and countrywide lockdown (inversely) were significantly associated with time dependent R0, but the contribution of countrywide lockdown to variability in R0 was over three times stronger as compared to that of temperature and wind speed combined. Thus, abating temperatures and easing lockdown may concur with increased transmissibility of COVID-19 in India.

collection. For this, we used two methodsthe exponential growth (EG) and the maximum likelihood (ML). Second, we estimated the daily R 0 in a time-dependent (TD) fashion. All estimates of R 0 require a knowledge of serial interval, the time difference between onset of symptoms in an infector and an infectee. We assumed a gamma distributed serial interval with a mean of 3.96 days and a standard deviation of 4.75 days (Du et al., 2020b) . We used the R package R 0 (Obadia et al., 2012) to derive all the estimates of R 0 . Finally, we considered the possibility of biased estimates of R 0 owing to the relative lack of testing facilities, especially during the initial period of the epidemic. For this, we used the algorithmic method (Lachmann et al., 2020 ) that considers South Korea as the reference country and estimates the degree of undertesting by combining demographic and vital statistics data. Using this method, we derived the possible undertesting on each study day.

Our analyses used estimates of R 0 as the dependent variable and the geo-meteorological and socio-behavioral characteristics as the explanatory variables. To compare groupwise means we used the Mann-Whitney U test or Kruskal-Wallis test as appropriate. Significance of heterogeneity across study locations was statistically tested using the Q test. Time series data were smoothed using a five-day sliding window. Further, to make the different time series (each meteorological characteristic) comparable, we converted them to a series of z-scores. To test the Supplementary Figure 1.

We first estimated the R 0 based on case counts reported for the entire country as well as only for the locations included in this study. For each of these datasets, we estimated the R 0 in two waysfirst based on the actual reported case counts and second by inflating the case counts to account for the potential undertesting on each day. The results of these analyses are shown in Figure 2 and referred to as unadjusted (actual case counts, blue bars) and adjusted (for potential undertesting, purple bars). Our average estimates of R 0 using different methods of estimation and with or without adjusting for undertesting ranged from 1.18 to 1.27 for India and 1.15 to 1.28 for the selected study locations. All the estimates and their 95% confidence intervals (error bars in Figure 2 ) were significantly above unity. Thus, the average estimates of R 0 were significantly greater than one, confirming the existence of the epidemic; the average R 0 estimates were only moderately above unity; the average R 0 estimates were minimally influenced by potential undertesting; and that the study locations yielded average R 0 estimates consistent with those for the whole country thereby indirectly reaffirming the representativeness of the selected study locations.

We also examined the heterogeneity of the average R 0 estimates across the study locations. For these analyses, we restricted the locations which showed at least seven consecutive days with a contiguous segment of non-zero cases. Total of 35 locations were eligible based on this criterion.

The average R 0 estimates derived using the ML method [point estimates and confidence intervals (CI)] for these 35 locations are shown in Table 1 . There was a significant heterogeneity in the J o u r n a l P r e -p r o o f Journal Pre-proof average R 0 estimates (Q = 224.28, degrees of freedom = 34, p = 6.9x10 -30 ) with estimates ranging from 1.98 for Dehradun to 0.89 for Kolkata. The average R 0 estimates for the top five contributing locations were: Mumbai 1.16 (95% CI 1.14 -1.18); Delhi 1.25 (95% CI 1.23 -1.28); Chennai 1.20 (95% CI 1.17 -1.23); Ahmedabad 1.10 (95% CI 1.07 -1.13) and Pune 1.22 (95% CI 1.18 -1.26).

We examined the potential association of geographical characteristics with the estimated R 0 . For this we conducted a multivariable regression model which included population density (a composite variable that includes information on population and area), elevation, latitude and longitude (to test for any cline effect). The results of these analyses are shown in Table 2 . These results showed that none of the correlates was significantly associated with the estimated R 0 .

Next, we considered the variability in R 0 estimates over the duration of the study for all locations together. Figure 3 shows that the R 0 estimates were initially high but undulated widely and gradually converged towards the overall estimates shown in Figure 2 with narrow confidence bands later. Thus, the time dependent R 0 estimates showed considerable variation across study time.

We examined the association of the time-dependent R 0 estimates with two socio-behavioral characteristicsimplementation of a countrywide lockdown and the extent of social distancing as reflected by the cellphone mobility data. When contrasted against the various phases of countrywide lockdown in India (grey shaded regions in Figure 3 ), we found that the median R 0 J o u r n a l P r e -p r o o f Journal Pre-proof estimates consistently reduced as lockdown was imposed. Before the lockdown began (March 1 through March 24, 2020) the median R 0 was 1.54 and this estimate decreased to 1.40 (March 25 -April 14, 2020), 1.21 (April 15 -May 3, 2020), 1.16 (May 4 -May 17, 2020) and 1.10 (May 18, 2020 onwards) during the lockdown phases 1 through 4, respectively (Kruskal-Wallis p <0.0001).

The cellphone-based community mobility data also revealed consistent and interesting patterns. Figure 2 , the overall trends in community mobility for all five destinations showed a dramatic decrease around the beginning of phase 1 lockdown, remained very low during phase 1 lockdown and then gradually increased as the lockdown progressed. The 5-day rolling z-scores for the average mobility based on these five parameters is shown in Figure   3 (green curve). This curve showed a dramatic reduction in mobility just prior to and during the first two phases of the lockdown. The curve showed an increasing trend in phases 2 and 4 of the lockdown.

The time trends for air temperature, relative humidity, air pressure, wind speed and rainfall are shown in Figure 4A . Over the duration of the study, air temperature and wind speed steadily increased; relative humidity and air pressure gradually decreased while rainfall remained steady.

As a first step of the association analyses, we estimated the cross-correlation between each meteorological variables and the R 0 estimates. Figure 4B shows the cross-correlograms for lags ranging from -10 to 10 days. We found that higher temperature, wind speed and rainfall were correlated inversely while relative humidity and air pressure were correlated positively with time J o u r n a l P r e -p r o o f Journal Pre-proof dependent R 0 estimates. The best cross-correlation was observed for temperature and humidity on the same day (-0.73 and 0.63, respectively), wind speed on previous day (-0.40), rainfall preceding by 4 days (-0.29) and air pressure preceding by 6 days (0.54). Together these results indicated that concurrent or immediately preceding values of meteorological variables are significantly correlated with time dependent R 0 estimates.

We then examined whether the meteorological and socio-behavioral covariates were independently associated with time dependent R 0 estimates. The full regression model used time dependent R 0 estimates as the dependent variable and following 14 covariates as explanatory variables: five z-scores for the meteorological covariates, five z-scores for community mobility data and four phases of lockdown (each used as a dichotomous variable). The results of these analyses are shown in Table 3 . In the full model, we observed that the lockdown phases 3 (only marginally) and 4 and wind speed were the only covariates that were statistically significantly associated with R 0 estimates. In this context, the mobility data (which was highly correlated with the lockdown phases) did not retain statistical significance. However, considering the potential for interactions among covariates and the possibility of an underpowered full model (14 covariates observed on 92 days), we conducted stepwise regression modeling with a probability retention criterion of 0.05. The results of the final model (Table 3) showed that air temperature zscore, wind speed z-score and lockdown phases 2-4 were retained in the final model. This model From the point of public health relevance, we then quantified the contribution of each variable retained in the final model to the overall variance of time dependent R 0 . The PRE estimates for the variables retained in the final model were as follows: air temperature: 9.1%, wind speed: 9.9%, lockdown phase 2: 12.2%, lockdown phase 3: 22.5% and lockdown phase 4: 27.0%. These results indicate that while the meteorological factors of air temperature and wind speed were statistically significant predictors of COVID-19 transmissibility, their contribution to dampening the R 0 estimate was 3-4 times weaker as compared to the countrywide lockdown phases 2-4.

Using nationally representative data from India over a three-month period, our study made three cardinal observations. First, the average basic reproduction rate (R 0 ) of COVID-19 infection in the period from March 1 through May 31, 2020 ranged from 1.15 to 1.28 even after accounting for the potential undertesting. Second, the COVID-19 transmissibility was significantly associated with daily average air temperature (inversely), daily average wind speed (positively) and the countrywide intervention of lockdown (inversely). Third, the contribution of lockdown to the variability in time dependent R 0 was three times more than the contribution of air temperature and wind speed combined. We did not observe a statistically significant association of any geographic characteristic with R 0 . Together, these results suggest that in India while the meteorological determinants of COVID-19 were independently associated with the transmissibility, their contribution was outweighed by that of the countrywide lockdown.

Even though statistically significantly greater than unity, our estimate of R 0 was low. This estimate is comparable to the value of 1.32 reported by others (Du et al., 2020b) . However, the J o u r n a l P r e -p r o o f Journal Pre-proof low value of R 0 should be interpreted with caution. First, there has been a debate about the length of serial interval with values ranging from as low as 3 days to as high as 9 days. (Du et al., 2020a; Du et al., 2020b; Ganyani et al., 2020; Moradi and Eshrati, 2020; Nishiura et al., 2020; Zhang et al., 2020) We used the serial interval of ~4 days which is on the lower side of the reported range and could have partly contributed to the low R 0 observed in this study. Second, the major part of the study period included lockdown and reduced mobility and therefore the R 0 estimate may represent a muted transmissibility owing to interventions in place. Third, the low R 0 estimate does not indicate lack of viral infectiousness or any other viral characteristic but only implies the extent of potential spread of the disease (Delamater et al., 2019) . Fourth, the epidemic of COVID-19 is still ongoing and our estimate of R 0 only captures the initial, ascending limb of the epidemic curve. Therefore, this R 0 estimate does not fully capture the population dynamics of COVID-19. Fifth, our estimate of R 0 is a conglomerate of the varying estimates across the study locations as shown in Table 1 . The variability in R 0 across study locations indicates that the location-specific epidemic curves were not aligned to the same starting point in time and therefore our R 0 estimate should not be used as a generalizable estimate of COVID-19 transmissibility. The reason for estimating R 0 in the study was to investigate the potential influence of geo-meteorological factors on transmissibility.

Several researchers around the world have demonstrated an inverse relationship between air temperature and number of COVID-19 cases (Demongeot et al., 2020; Guo et al., 2020; Harmooshi et al., 2020; Jahangiri et al., 2020; Malki et al., 2020; Pramanik et al., 2020; Ran et al., 2020; Ren et al., 2020; Seligmann et al., 2020a; Steiger et al., 2020) . Our results are in agreement with the general understanding that higher ambient temperature can inversely J o u r n a l P r e -p r o o f Journal Pre-proof influence COVID-19 transmissibility (Guo et al., 2020; Jahangiri et al., 2020) . Our study duration marks a period of increasing temperature in the Indian peninsula and our results indicate that, in general, high ambient temperatures were associated with lower R 0 estimates such that one standard deviation increase in air temperature was associated with a 0.08 lower R 0 (Table 3, On the other hand, we observed that a unit standard deviation increase in wind speed was associated with a 0.08 higher R 0 (Table 3, (Eslami and Jalili, 2020) . On the other hand, moderate wind speed combined with high number of susceptible individuals can lead to an effective dispersion of the aerosols and may lead to a positive association between wind speed and COVID-19 transmissibility (Sahin, 2020). Our study cannot directly answer these interesting hypotheses, which should be tested in future studies. Knowledge of the biophysical aspects of observed ecological associations is mechanistically important and should be the focus of future studies.

Nonetheless, a head-to-head comparison indicated that the lockdown period was associated with three times stronger contribution to the variability in R 0 as compared to that of air temperature and wind speed combined. From the perspective of public health action, this observation supports the role of proactive interventions to de-escalate the transmissibility of COVID-19.

Conceivably, as the air temperature wanes and the lockdown eases, more cases of COVID-19 can be expected.

There is both a logical and biological support to expect a positive correlation between population density and COVID-19 transmission (Amoo et al., 2020; Jahangiri et al., 2020; Liu, 2020; Rashed et al., 2020; Rocklov and Sjodin, 2020; Tammes, 2020) . Our study failed to show an association between population density and COVID-19 transmissibility in a regression framework. There could be several explanations that partly account for the observed lack of association between population density and COVID-19 transmission. First, the geographical locations represented a more global than focal population density which will be immediate concern in COVID-19 transmission. Second, the duration of epidemic was not the same across all the locations studiedlocations in the lowest quartile of population density had an average observed duration of only 11 days while those in the highest quartile of population density had an average of 6 weeks of epidemic experience. A direct comparison of transmissibility across J o u r n a l P r e -p r o o f Journal Pre-proof gradients of population density can therefore be confounded. Third, travel historya major determinant of COVID-19 transmission (Cruz et al., 2020; Nussbaumer-Streit et al., 2020) was not included in this study. Conceivably, travel is more frequent to the metropolitan areas with high population density and therefore the epidemic will be slower to take off in low density locations. Together, these possibilities make it difficult to tease apart the potential role of population density in our study.

Our results should be interpreted in the light of some limitations. First, this was a retrospective analysis that combined data from different sources. The data are collected at the level of geographic locations and not at the level of individual patient. For example, person-to-person transmissibility of COVID-19 in an infector-infectee scenario was not investigated in this study.

Therefore, all the estimates and associations should only be considered as general patterns rather than definitive evidence. Second, akin to any observational study, unmeasured confounding can be expected to be operational. Third, it may appear surprising that the cross-correlations in time series analyses ( Figure 4B ) for air temperature, relative humidity and wind speed that highest values were on (or very close to) the day of time-dependent R 0 . It should be noted that the crosscorrelation shown in Figure 4B are for 5-day smoothed weather parameters. Still, our study generates the hypothesis that while the case counts in response to environmental fluctuations may take time to be altered, it is possible that the influence on transmissibility (R 0 ) is more immediate. Future studies need to specifically address this hypothesis.

We would like to stress that the observations made in this study relate to the initial spread of COVID-19 in India. As the epidemic enters subsequent phases, these observations and patterns can change. Such change of observed associations has been reported with regard to association with air temperature(Seligmann et al., 2020a), altitude (Seligmann et al., 2020b) and population J o u r n a l P r e -p r o o f Journal Pre-proof density (Seligmann et al., 2020a) . For example, based on data from 124 countries in the early phase and 28 countries in the second phase of COVID-19 epidemic, it was reported that COVID-19 transmission decreased with air temperature in the early phase but an inverse trend was observed during the second phase (Seligmann et al., 2020a) . This observation has been attributed to the mutant and better-adapted viruses that sprang the second wave of COVID-19 transmission.

The observations we report in this study refer to the initial phase and are consistent with the early phase observations Despite these potential limitations our study demonstrated interesting and important patterns of association of geo-meteorological factors in COVID-19 spread. To control a pandemic of the current magnitude, all scientific evidence from a holistic standpoint is needed.

To that end, our study provides clues into the ecological aspects of COVID-19 during the initial months in India. 

Term Effects of Ambient Ozone, PM2.5, and Meteorological Factors on COVID-19 Confirmed Cases and Deaths in Queens

Investigation of effective climatology parameters on COVID-19 outbreak in Iran

Nigeria and Italy Divergences in Coronavirus Experience: Impact of Population Density

Correlation between climate indicators and the coronavirus (SARS-CoV-2)? Centre for Evidence-Based Medicine

A spatio-temporal analysis for exploring the effect of temperature on COVID-19 early evolution in Spain

Persistence of SARS-CoV-2 in the environment and COVID-19 transmission risk from environmental matrices and surfaces

Exploring the young demographic profile of COVID-19 cases in Hong Kong: Evidence from migration and travel history data

Examine the impact of weather and ambient air pollutant parameters on daily case of COVID-19 in India

Complexity of the Basic Reproduction Number (R0)

Temperature Decreases Spread Parameters of the New Covid-19 Case Dynamics

COVID-19 serial interval estimates based on confirmed cases in public reports from 86 Chinese cities

Serial Interval of COVID-19 among Publicly Reported Confirmed Cases

Air pollution and COVID-19: Is the connect worth its weight?

Impact of temperature on the dynamics of the COVID-19 outbreak in China

Impact of weather indicators on the COVID-19 outbreak: A multi-state study in India

Causal analysis of COVID-19 observational data in German districts reveals effects of mobility, awareness, and temperature

Influence of socio-ecological factors on COVID-19 risk: a cross-sectional study based on 178 countries/regions worldwide

Social distancing, population density, and spread of COVID-19 in England: a longitudinal study

Correlation between weather and Covid-19 pandemic in Jakarta

COVID-19 and Environmental factors. A PRISMAcompliant systematic review

Airborne transmission of severe acute respiratory syndrome coronavirus-2 to healthcare workers: a narrative review

No association of COVID-19 transmission with