Infectious diseases have caused profound disruptions throughout the history of humanity. Despite a decrease in the number of deaths attributed to contagious diseases, there has been a constant rise in the number of outbreaks over the past few years due to emerging and re-emerging infectious agents [1]. Influenza, dengue fever, and HIV/AIDS are the three leading contagious diseases that have infected millions of people globally [2]. In addition to these, approximately 215 different infectious agents have caused 12,102 outbreaks in 219 countries over the last 30 years [3]. In general, there have been significant advances in the treatment and curing of infectious diseases. However, infectious diseases pose a considerable challenge to the health system due to their frequency, infectivity, and mobility in today’s extensively interconnected world. Therefore, early detection and prevention of infectious diseases continues to be a top priority among the global health community.

The current COVID-19 pandemic has disrupted and overwhelmed health systems worldwide. COVID-19 is an infectious disease that is caused by a newly discovered coronavirus, and the main route of transmission is thought to be through respiratory droplets [4]. The index case of COVID-19 was traced to December 1, 2019, in Wuhan, China [5]. The aggressive nature of the spread of COVID-19 led to its declaration as a “public health emergency of international concern” and then a pandemic by the World Health Organization (WHO) on January 30 and March 11, 2020, respectively. As per the WHO, 216 countries had reported more than 121 million cases and 2.6 million deaths due to COVID-19 as of March 17, 2021 [6]. There were 11.4 million confirmed cases in India alone, with 0.16 million deaths, and it is among the most severely affected countries to date [7]. Due to a lack of effective treatment strategy, nonpharmaceutical interventions (NPIs), such as restricted mobility, home quarantine, and lockdown measures, were enforced worldwide to halt interhuman transmission of the virus [8]. As India is the second most populous country in the world, with suboptimal investment, NPIs were seen as the most crucial part of pandemic mitigation. Hence, the Government of India also implemented a countrywide lockdown to halt disease progression on March 24, 2020 [9].

Research has demonstrated the association between mobility and disease transmission for various infectious diseases, such as cholera, dengue, influenza, Ebola, malaria, measles, and COVID-19 [10-17]. NPIs are intended to slow the rapid disease transmission and contain the disease burden until effective pharmacological management options become accessible [18,19]. Implementing NPIs in response to infectious disease outbreaks is not a new method to limit mobility; they have been used for centuries [20,21]. More recently, such measures were implemented during the containment of the severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) epidemics, which occurred in the last decades [22,23].

Although the connection between mobility and disease has been known for centuries, establishing this causal association is challenging, as measuring and quantifying the levels of mobility at the population level is difficult. This can be attributed to the challenges in obtaining access to mobility and disease data. However, numerous mathematical models have demonstrated such associations between mobility and infectious disease transmission dynamics [24-26]. Moreover, during the current pandemic, the digital ecosystem has supplemented traditional surveillance to provide data about disease severity and mobility in real time.

Given the highly infectious nature of COVID-19, the importance of digital epidemiology could be felt in disease containment [24-27]. Digital epidemiology is a branch of epidemiology that uses data generated outside the public health system [28]. Google Flu and Google Trends have been successfully used to study various communicable and noncommunicable diseases [29,30]. On similar lines, Google released Community Mobility Report (CMR) data collated from people who accessed its applications using mobile and handheld devices. The restriction in mobility by the Indian government and the data availability provides researchers with an opportunity to empirically study the relationship between social activity, mobility, and COVID-19 incidence. However, there is a shortage of scientific literature that documents the use of these data for surveillance purposes. Very few researchers have tried to correlate mobility trends with the aggressiveness of the disease [31-34]. Sulyok and Walker [31] depicted negative correlations between CMR data and case incidence for major industrialized countries of Western Europe and North America. Wang and Yamamoto [32] also depicted that a model using CMR data can describe the combined effects of mobility at the local level and human activities on the transmission of COVID-19. Cot et al [33] analyzed Google and Apple mobility data. They concluded that a substantial decrease in the infection rate occurred 2-5 weeks after the onset of mobility reduction [33]. None of these studies explored the association of mobility with any other epidemiological indicators except disease incidence; meanwhile, it has been established that disease incidence alone is not an ideal measure for making comparisons [35]. Therefore, in this study, we attempt to understand and explore the role of mobility in spreading COVID-19 infection in India using mobility data from Google. During the pandemic, the central government has issued various health advisories; however, because health is a state responsibility, the final implementation of those instructions depends on the state itself. Therefore, we hypothesized that the states with strict enforcement of lockdown would witness fewer cases and vice versa. Hence, we have also examined the states with the maximum and minimum numbers of cases for changes in mobility as per CMR data.

We used the CMRs provided by Google to assess the national and subnational patterns of mobility before, during, and after the COVID-19 pandemic lockdown enforced by the government of India and their correlations with disease severity. There are specific critical findings in our study. First, there were marked interstate variations in the disease burden during the three phases of our study period. By the end of the lockdown phase, although the CPM and DR continued to increase, disease severity, as depicted by the CFR, started to decrease. The CMR data depicted that mobility decreased during the lockdown and then increased again during the unlock phase. We observed intramobility solid patterns among the 6 mobility indicators. Residential mobility was seen to be inversely associated with mobility in public places. A significant correlation was seen between mobility and epidemiological indicators.

Inter- and intramobility networks play significant roles in disease transmission dynamics in the modern era [44]. We observed wide subnational variations in the disease burden, as depicted by various epidemiological indicators used in the study. The state of Maharashtra was among the most greatly affected states in the country. This can be attributed to its large population size, as it is the second most populous state in India after Uttar Pradesh. Moreover, COVID-19 was a relatively urban phenomenon during the study period, and Maharashtra is one of India's most urbanized states (more than 50% urbanized). More than half of the COVID-19 cases in Maharashtra were reported from four major cities: Mumbai, Thane, Pune, and Nagpur. In contrast, the proportion of urbanization in other populous states such as Uttar Pradesh is 22%. Also, Maharashtra attracts more people from other states for education and jobs; hence, it has a very high population density. This demographic profile has a significant impact on the COVID-19 transmission dynamics. However, if we consider total CPM, many other states, such as Goa, Delhi, and Andhra Pradesh, reported more cases than Maharashtra by the end of lockdown. Also, any state with an efficient and sensitive surveillance system in place will detect more cases during an epidemic. Maharashtra has always been among the top performers in India in this regard [45]. On the other hand, the state of Sikkim depicted the minimum number of cases throughout the country. Sikkim is a remote state in a hilly area, with a small population size and lower population density, fewer migrations, more rural areas, and less interstate trade and transit; this explains its lower number of cases during the study period.

In our study, residential mobility increased during the lockdown. It also correlated negatively with other measures of mobility. These findings are consistent with studies conducted by Saha et al [34,46], which found that people stayed at home during the lockdown. The mobility trends display that mobility started to decrease even before the government implemented the lockdown measure. Although legal enforcement was the prime reason for reducing mobility, people also restricted their movements voluntarily and avoided crowded places due to apprehensions regarding the disease [47-49]. Mobility in other places was reduced during the lockdown and then gradually rescaled during the unlock phase. This pattern is coherent with those in other studies, which found that people rescheduled or canceled travel and transport plans in the wake of public health emergencies [50,51].

We used the Kendall tau correlation to quantify the relationship between mobility and epidemiological indicators, as this correlation is more robust and consistent for nonnormal data. The mobility indicators depicted strong correlations with epidemiological characteristics for both the lockdown and unlock phases. This trend is consistent with many theoretical studies that have predicted the role of mobility for infectious diseases [10,52,53]. Previous studies have demonstrated the utility of mobility in the spread of COVID-19 data globally [31,54]. Our analysis indicates that mobility is a potential metric to monitor and predict disease outbreaks. However, the statistical analysis is exploratory and univariate and requires rigorous statistical evaluation before mobility can be adopted as an indicator of surveillance.

Moreover, our models depicted wide interstate variations in mobility patterns. We discussed variations only in the states of Maharashtra and Sikkim, as they reported the maximum and minimum numbers of cases, respectively, at the end of lockdown to understand the relationship between mobility and disease dynamics. Addressing diseases such as COVID-19 from a mathematical perspective can reveal the internal pattern and potential structure of pandemic control, and it can help provide insights into the transmission dynamics of such diseases and the potential role of different public health intervention strategies [55]. Lockdown interventions to prevent the spread of infection lead to different patterns of mobility. However, lockdown measures only serve their purpose when they ate strictly enforced. Our data suggest that the growth trajectory for the rise in cases was linear compared to the steep trajectory post lockdown. Many authors have previously discussed the impact of lockdown in controlling the spread of COVID-19 in India [56,57]. However, lockdown measures to save lives were recommended and championed by the WHO and other leading agencies. There are numerous discussions and debates in the literature regarding the appropriateness of total lockdown measures [58-60]. A group of medical researchers published the “Great Barrington Declaration,” in which they emphasized the concept of “Focused Protection” as an alternative to lockdowns [61]. Simultaneously, other researchers disagreed and called for strict measures until a vaccine became available; they published the “John Snow Memorandum” [62]. However, it may take a long time to assess the overall strengths and shortcomings of the lockdown.

This study is the first pan-Indian empirical study quantifying the role of mobility in disease transmission. However, there are some obvious limitations to our study. The major limitation is the dynamic nature of COVID-19 and the mobility patterns. Therefore, it is challenging to obtain robust estimates unless disease transmission stabilizes. Moreover, an ecological study uses normative mobility data; this study may thus be impacted by ecological fallacy. The disease infection rate varies per gender, accessibility to health care, and literacy level; however, the data for the current study limit its generalization to these subgroups.

Similarly, no attempt can be made to examine the psychological and sociological issues affecting mobility. The data used by Google to generate the mobility estimates may have questionable concordance with the actual mobility rates. Mobile phones may not reflect the actual mobility in the community, especially in rural areas, where GPS-enabled smartphones are not used by many people. Similarly, apprehensions about data misuse may prevent many smartphone users from using maps, undermining the actual estimates. Less frequent GPS usage may be a reason why we could not find intermobility patterns for Sikkim in our analysis. Finally, per CMR, baseline dates do not account for the seasonality of movements. The lack of accounting of seasonality may also affect the accuracy and precision of estimates. Moreover, Google’s CMR data do not directly equate to some specific COVID-19 control measures. We could not assess the reasons underlying the patterns observed in mobility.

To conclude, we can use mobile-based open-source mobility data to assess the effectiveness of social distancing. CMR data depicted an association between community mobility with disease severity indicators. We suggest that data related to community mobility can be of utility in future COVID-19 modeling studies. With the declaration of COVID-19 as a pandemic, mobility levels declined, which can be primarily attributed to legal enforcement or increased fear of disease leading to personal behavioral changes. Google’s CMR depicts the effect of these measures on community movement. CMR can provide an effective tool for the authorities to evaluate the timing and impact of social distancing efforts, mainly related to movement restrictions. We recommend using these data whenever applicable to supplement the existing surveillance methods in any country. This approach does not involve any additional cost and can provide quick action points about the adherence to social distancing measures. This method can be used to forecast mass movements during nonpandemic conditions, such as the famous gatherings during Kumbh Mela in India, and can help us assess preparedness accordingly. An attempt can also be made to forecast mass movements, which is needed to make informed decisions. With the increase in mobile internet usage, the real-time data method is expected to increase accuracy. Future studies should focus on establishing the cultural, social, and economic issues that are responsible for some of the differences in adherence to social distancing measures.