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• W3092931661 abstract "Article19 October 2020Open Access Transparent process COVID-19 pandemic-related lockdown: response time is more important than its strictness Gil Loewenthal Gil Loewenthal orcid.org/0000-0003-4029-216X The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Shiran Abadi Shiran Abadi orcid.org/0000-0002-3932-6310 School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Oren Avram Oren Avram orcid.org/0000-0003-1984-2139 The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Keren Halabi Keren Halabi School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Noa Ecker Noa Ecker The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Natan Nagar Natan Nagar The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Itay Mayrose Corresponding Author Itay Mayrose [email protected] orcid.org/0000-0002-8460-1502 School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Tal Pupko Corresponding Author Tal Pupko [email protected] orcid.org/0000-0001-9463-2575 The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Gil Loewenthal Gil Loewenthal orcid.org/0000-0003-4029-216X The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Shiran Abadi Shiran Abadi orcid.org/0000-0002-3932-6310 School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Oren Avram Oren Avram orcid.org/0000-0003-1984-2139 The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Keren Halabi Keren Halabi School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Noa Ecker Noa Ecker The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Natan Nagar Natan Nagar The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Itay Mayrose Corresponding Author Itay Mayrose [email protected] orcid.org/0000-0002-8460-1502 School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Tal Pupko Corresponding Author Tal Pupko [email protected] orcid.org/0000-0001-9463-2575 The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel Search for more papers by this author Author Information Gil Loewenthal1,‡, Shiran Abadi2,‡, Oren Avram1,‡, Keren Halabi2,‡, Noa Ecker1,‡, Natan Nagar1, Itay Mayrose *,2 and Tal Pupko *,1 1The Shmunis School of Biomedicine and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel 2School of Plant Sciences and Food Security, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel ‡These authors contributed equally to this work *Corresponding author. Tel: +972 3 640 7212; Fax: +972 3 640 9850; E-mail: [email protected] *Corresponding author. Tel: +972 3 640 7693; Fax: +972 3 640 9245; E-mail: [email protected] EMBO Mol Med (2020)12:e13171https://doi.org/10.15252/emmm.202013171 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract The rapid spread of SARS-CoV-2 and its threat to health systems worldwide have led governments to take acute actions to enforce social distancing. Previous studies used complex epidemiological models to quantify the effect of lockdown policies on infection rates. However, these rely on prior assumptions or on official regulations. Here, we use country-specific reports of daily mobility from people cellular usage to model social distancing. Our data-driven model enabled the extraction of lockdown characteristics which were crossed with observed mortality rates to show that: (i) the time at which social distancing was initiated is highly correlated with the number of deaths, r2 = 0.64, while the lockdown strictness or its duration is not as informative; (ii) a delay of 7.49 days in initiating social distancing would double the number of deaths; and (iii) the immediate response has a prolonged effect on COVID-19 death toll. Synopsis This study used mobility data obtained from Apple users around the world to explore the effect of lockdown on COVID-19 related mortality. The results suggest that countries that enforced a strict lockdown could have obtained similar mortality figures with less stringent mobility restrictions. The time at which social distancing was initiated is highly correlated with the number of deaths. No association was observed between the lockdown strictness (or its duration) to the number of deaths. A delay of 7.49 days in social distancing initiation would double the number of deaths. The immediate response has a prolonged effect on COVID-19 related mortality. The paper explained Problem In order to curb the spread of the COVID-19 pandemic, governments around the world have enforced mobility restrictions on their citizens. These mobility restrictions included, for example, closure of non-essential businesses and prevention of public gatherings and led to serious socioeconomic consequences. We wished to understand the impact of mobility restriction on mortality rate, by comparing mobility and mortality data across countries around the world. Results We analyzed mobility volume obtained from cellular usage of Apple users from many countries around the world to quantify country-specific lockdown characteristics, such as, social distancing start time, lockdown timing, lockdown strictness, lockdown duration, and lockdown release rate. We crossed the different characteristics with the observed mortality rate of each country. Our analysis suggests that the time at which social distancing was initiated had a critical and long-term effect: a delay of 7.49 days in lockdown commencement is associated with a doubling of the expected number of deaths. This is in contrast to other parameters such as the lockdown strictness that had negligible impact on mortality. Impact Countries that enforced a very strict lockdown could have obtained similar mortality figures with less stringent mobility restrictions as long as social distancing is initiated as early as possible after the first incidents are recorded. As a direct consequence, the socioeconomic damage of a strict lockdown could have been less severe. Introduction In 2020, the coronavirus pandemic has rapidly spread around the globe, threatening health and economical systems. At first, many governments attempted to minimize exposure to the virus by limiting cross-border arrivals. However, the rapid person-to-person transmission rate of the virus (Chan et al, 2020; Li et al, 2020) required that more severe measures be taken to plummet infection frequencies. Governments that used lockdown to enforce social distancing varied in their policy, timing, and duration, in particular relative to the mortality rate in their country. For example, Italy enforced a severe, nationwide, lockdown on March 10, when over 35,000 confirmed cases and almost 3,000 deaths had already been recorded. In other countries, lockdown policies were embraced at earlier stages in attempt to prevent severe outbreaks. Israel, for instance, reached the strict lockdown on March 19 with a relatively low number of 648 confirmed cases and no deaths to that day. In contrast, several countries, such as Sweden and Japan, advocated social distancing but did not enforce a lockdown as a means of coronavirus spread prevention. How can social distancing be quantified? One could measure governmental regulations such as the permitted walking distance from the residence, limitations on mass gatherings, school closures, and whether people were allowed to attend their workplaces. For example, Hu et al (preprint: 2020) suggested a score that takes into account various governmental interventions in the United States. This score was used to predict future infections depending on the intervention level. While this model may be useful when governmental decisions are made, it does not reflect whether social distancing has been implemented de facto (preprint: Kohanovski et al, 2020). Soures et al (preprint: 2020) used data collected from navigation applications on mobile cellphones together with past infection rates to predict future infection rates. These predictions were based on a neural-network model, in which the connection between mobility data and infection rates is hard to interpret and thus, practically, cannot be converted into tangible measures for the arms race against the disease. It is currently unknown which aspects of the lockdown (e.g., duration, strictness, timing from onset of death cases) affect mortality rates. Understanding the linkage between the lockdown dynamics and COVID-19 death incidents is highly important for balancing between health, welfare, and economy. Location data collected from mobile phone calls have previously been linked with the identification of pandemic outbreaks, e.g., the 2005 cholera outbreak in Senegal (Finger et al, 2016). With the spread usage of smartphones nowadays, location and mobility data are routinely collected by numerous service providers. Mobility data from such datasets were shown to be associated with COVID-19 hotspots of disease transmission and spread (Badr et al, 2020; Benzell et al, 2020; Bonaccorsi et al, 2020; Kraemer et al, 2020; Linka et al, 2020; Pepe et al, 2020; preprint: Soures et al, 2020). Here, we develop parametric models that quantify trends related to mobility and mortality and fit them to all OECD countries. Using these models, we demonstrate that the correlation between the timing in which the social distancing was initiated and the COVID-19-related deaths is r2 = 0.64 across the OECD countries excluding Japan (that was previously reported as an exception with respect to the spread of the disease, e.g., by Iwasaki & Grubaugh, 2020). In contrast, the severity of the lockdown and its duration are not as informative for explaining mortality rates. Our analysis thus suggests that a moderate lockdown, rather than a very strict one as was imposed by most countries, should be sufficient to curb COVID-19-related mortality, as long as action is taken in the appropriate time frame. Results Following the COVID-19 outbreak, Apple Inc. has started publishing daily reports regarding people mobility, collected from usage of maps on mobile cellphones (Data ref: Apple, 2020). We used these mobility data, denoted as M(t), to quantify the actual commencement of the lockdown as a function of time in different OECD countries. We collected daily death incidents across time and overlaid them on the mobility data (see Fig 1A for the United Kingdom as a representative OECD country and Appendix Fig S1 for all OECD countries). We observed that the trend of daily deaths stabilized and subsequently decreased several days after a sharp mobility drop, typically observed in March, corresponding to the time of applying governmental interventions. During the time period between January and May, most countries enforced social distancing as a strategy to handle the initial outbreak. Following this period, with the accumulation of additional knowledge regarding means of prevention and treatment (Sanders et al, 2020; Xu et al, 2020) and as many countries started to relax the restrictions and ease the lockdown, the mobility trends across countries have diverged. For example, the mobility trend in Israel returned to the baseline and did not dramatically fluctuate after May, while in Sweden it rose beyond the baseline and declined back toward August (see Appendix Fig S1 for the trends of the OECD countries between January and August). Figure 1. Modeling mobility data A. Daily mobility data, M(t), (orange line, left y-axis) overlaid with daily deaths (blue line, right y-axis) for the United Kingdom during the lockdown period (January 13 to May 10). M(t) is given as percentages relative to that recorded on January 13, which serves as the baseline. For the data of all OECD countries until August 31, see Appendix Fig S1. B. An illustration of the mobility model and its free parameters: L—mobility difference between routine and lockdown; k—drop steepness; t0—drop midpoint; b—mobility level during lockdown; t1—release day; a—recovery rate. t′ represents the Social distancing start time, and t″ represents the Minimal mobility time point, corresponding to the times before and after the mobility drop, respectively. Download figure Download PowerPoint Mobility analysis To model the social distancing dynamics during the initial phase of the pandemic, we focused on the time period between January 13 and May 10 (termed the “lockdown period” hereafter). Inspection of the mobility trends during this time period revealed four phases: (i) a stable phase of high mobility (with fluctuations on weekends); (ii) a sharp drop (suggesting social distancing has actually started); (iii) a period of low mobility; and (iv) a gradual incline toward a normal routine (Fig 1). Phases (i)-(iii) resemble a (mirrored) logistic function and phase (iv) is approximately linear. We modeled this overall trend by assembling a logistic function and a linear one as a function of time (t, given in days): M ^ ( t ) = L 1 + e − k ( t − t 0 ) + b t ≤ t 1 a ( t − t 1 ) + M ^ ( t 1 ) t > t 1 The six free parameters of this model are illustrated in Fig 1B. Fitting the mobility model to the 37 OECD countries resulted in an average r2 of 0.9 between the observed data and the fitted functions (all P values < 10−32, Appendix Table S1). The inferred model parameters enabled the comparison of several informative features for the different countries (see Materials and Methods). As examples, we present the fitted models for five representative OECD countries: Germany, Israel, Italy, Spain, and Sweden (Fig 2; for the inferred features and fitted models of all countries see Appendix Table S2 and Appendix Fig S2). Our results demonstrate that while the lockdown strictness varied considerably, all countries reached some form of a lockdown by the middle of March 2020, with Spain presenting the most intense drop (88%). The social distancing start time in Italy occurred earlier, on February 25 compared with March 6–9 for the abovementioned four other countries. Nevertheless, the mobility in Italy declined in a relatively gradual manner with respect to other examined countries, as the drop duration lasted 20 days. The extent of mobility reduction in Germany (59%) was relatively low compared to other countries in which a lockdown was issued, and a gradual return to normal routine was initiated right after the lowest mobility level was reached. Even though a lockdown was not regulated in Sweden, the data and model demonstrate that social distancing indeed happened, as a drop of 29% was observed followed by a moderate return back to routine (lockdown release rate of 0.57). Figure 2. The fit of the mobility model for five representative OECD countriesA–F Colored lines in panels (A-E) represent the mobility model M ^ ( t ) fitted to the mobility data M(t) (gray lines). The optimized parameters are indicated. Panel (F) presents the overlay of the five fitted models. The two-letter codes and the five colors correspond to the countries represented in panels (A-E) (countries abbreviations are denoted in the titles of the panels). The x-axes represent days from January 13 to May 10, 2020. The y-axes represent the percentage change in mobility. For the parameter values and the inferred features of all 37 countries, see Appendix Tables S1 and S2. Download figure Download PowerPoint COVID-19 mortality We examined the effect of the extracted mobility features on the dynamics of the mortality levels during the lockdown period. We focused on the lockdown period to examine the effect of the lockdown as the main measure, without the effect of other obscuring means of prevention that were learned and adopted after the lockdown was eased. Notably, toward the end of the lockdown period, different countries were at different phases of the daily mortality trends. For example, Greece and Australia reached only few daily new death cases, while in Germany and Italy the decline was more gradual and in Mexico and Columbia the trends were still elevating (Appendix Fig S1). To compute the expected mortality rate across time, we fitted a logistic function, denoted as D ^ ( t ) , to the accumulated number of COVID-19 deaths of each country across time, D(t): D ^ ( t ) = L d 1 + e − k d ( t − t d 0 ) as in Tátrai and Várallyay (preprint: 2020). The parameters Ld, kd, and t d 0 are similar to those defined for the mobility model and represent the total expected mortality at the end of the pandemic, the mortality increase rate, and the time the cumulative mortality has reached its midpoint, respectively. This enabled to compute the COVID-19 Mortality Probability, namely, the expected mortality normalized by the population size of each country. The fitting of D ^ ( t ) to D(t) across countries resulted in an average r2 of 0. 99 (max P value = 1e-96; Appendix Table S3; see Fig 3 for examples of Israel and Japan and Appendix Fig S2 for all countries). Figure 3. Synchronizing between the mortality model and the mobility modelA, B The dark orange plots represent the mobility model, M ^ ( t ) , fitted to the mobility data, M(t) (light orange; left y-axis) of (A) Israel and (B) Japan. The dashed vertical orange line represents the social distancing start time, predicted by the mobility model. The dark blue plots represent the mortality model, D ^ ( t ) , fitted to the accumulated death data, D(t) (light blue; right y-axis). The dashed vertical blue lines represent the day ten deaths were documented. τ represents the time difference between the orange and the blue vertical lines, defined as the response time (τ is negative for Israel and positive for Japan). The graphs for all OECD countries are given in Appendix Fig S2. Download figure Download PowerPoint Association between mobility and mortality data We computed the response time of each country, τ, defined as the difference between the social distancing start time and the day in which ten first deaths were recorded. Fig 3 demonstrates the computation of τ for Israel and Japan (τ = −19.83 and 18.16 days, respectively; see Appendix Fig S2 and Appendix Table S4 for all countries). While a negative τ was inferred for most countries, a positive τ was inferred for five countries (France, Italy, Japan, Spain, and the United States), indicating that social distancing started after ten COVID-19 deaths were documented (Fig 4, Appendix Fig S2). We observed a significant correlation between τ and the log COVID-19 Mortality Probability (r2 = 0.38, P value = 1e-4). Previous reports have discussed the abnormally low mortality rate in Japan (Iwasaki & Grubaugh, 2020); thus, we computed the correlation excluding Japan and obtained a substantial increase in correlation (r2 = 0.64, P value = 1e-8). Neither the lockdown strictness nor the lockdown duration was significantly correlated with log COVID-19 Mortality Probability (Table EV1). Figure 4. A semi-logarithmic scatter plot of the COVID-19 Mortality Probability and τThe x-axis represents τ, the difference between the social distancing start time and the day in which the ten first deaths were recorded for the respective country (intuitively, the response time). The y-axis represents the COVID-19 Mortality Probability in a logarithmic scale. Dot sizes are proportional to population sizes. The dashed line corresponds to the fitted regression, excluding Japan. For raw data, see Appendix Table S4. Download figure Download PowerPoint The high correlation between τ and the log COVID-19 Mortality Probability yielded a crucial implication, as it allowed inferring the time required for this probability to double. We fitted a linear regression to the data presented in Fig 4 (excluding Japan) and used the slope of the fitted regression line to compute the estimated time for doubling the COVID-19 Mortality Probability. Accordingly, our results indicate that a 7.49 days delay in lockdown commencement doubled the expected number of deaths (95% CI [6.02, 10.03]). This result, which emerged from a data-driven model, is in accordance with the results of an epidemiological-model based study (preprint: Pei et al, 2020), which concluded that 54% of the deaths in the United States could have been prevented if non-pharmaceutical interventions had been implemented a week earlier. We focused our analysis on 37 OECD countries, to concentrate on a representative group of relatively reliable reports. Nevertheless, our results sustain when including additional non-OECD countries or when concentrating on subregions for which sufficient data exist: the r2 between τ and the log COVID-19 Mortality Probability for 58 countries was 0.37 (P value = 4e-7; Fig EV1). A significant correlation was also observed when analyzing states within the United States (r2 = 0.36; P value = 8e-6; Fig EV2). We next examined whether our conclusions hold when the infection rate, rather than the mortality rate, is examined. To this end, we fitted D ^ ( t ) to the accumulated number of COVID-19 confirmed cases across time and computed the log COVID-19 Infection Probability, similar to the way the log COVID-19 mortality Probability was computed. A significant correlation of r2 = 0.47 was also observed between τ and the log COVID-19 Infection Probability in OECD countries (P value = 4e-6; Fig EV3, Table EV2). Notably, the infection rate is highly dependent on the COVID-19 test policy and thus varies across countries. Click here to expand this figure. Figure EV1. A semi-logarithmic scatter plot of the COVID-19 Mortality Probability and τ in 58 countriesThe x-axis represents τ, the difference between the social distancing start time and the day in which the first ten deaths were recorded for the respective country (intuitively, the response time). The y-axis represents the COVID-19 Mortality Probability in a logarithmic scale. Among the 63 for which mobility and death data exist, Cambodia, Hong Kong, Vietnam, Taiwan, and Macau did not reach ten deaths before May 10. Dot sizes are proportional to population sizes. Correlation r2 = 0.28 (P value = 2e-5) when including Japan and r2 = 0.37 (P value = 4e-7) excluding Japan. The dashed line corresponds to the fitted regression, excluding Japan log (COVID 19 Mortality Probability) = 0.035τ –3.89. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. A semi-logarithmic scatter plot of the COVID-19 Mortality Probability and τ for states within the United StatesThe x-axis represents τ, the difference between the social distancing start time and the day in which the first ten deaths was recorded for the respective country (intuitively, the response time). The y-axis represents the COVID-19 Mortality Probability in a logarithmic scale. Dot sizes are proportional to population sizes. Pearson r2 = 0.36 (P values = 8e-6). Wyoming and South Dakota were excluded due to insufficient data. The dashed line corresponds to the fitted regression, log (COVID 19 Mortality Probability) = 0.03τ–3.39. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. A semi-logarithmic scatter plot of the COVID-19 Infection Probability and τThe x-axis represents τ, the difference between the social distancing start time and the day in which the first 500 confirmed cases were recorded for the respective country (intuitively, the response time). The y-axis represents the COVID-19 Infection Probability, which was computed by fitting a logistic function to the daily confirmed cases, similar to the COVID-19 Mortality Probability (in a logarithmic scale). Dot sizes are proportional to population sizes. Pearson r2 = 0.18 (P value 8e-3) when including Japan and r2 = 0.47 (P value 4e-6) excluding Japan. The dashed line corresponds to the fitted regression, excluding Japan, log(COVID 19 Infection Probability) = 0.032τ–2.48. Download figure Download PowerPoint Prolonged impact of the initial response on the COVID-19-related mortality Evidently, the presented analysis corresponds to the lockdown taken as an initial response by most countries in the first several months of the pandemic. Next, we examined whether the effect of the initial response sustained over a prolonged time period. To this end, we extracted the reported mortality rates on August 31, 2020, and normalized them by the population size (termed, Aug-20 COVID-19 Mortality Probability). A significant correlation between τ, as computed from fitting M ^ ( t ) to the mobility data during the lockdown period, and the log Aug-20 COVID-19 Mortality Probability, was maintained (r2 = 0.62, P value = 1e-8 and r2 = 0.34, P value = 2e-4 when excluding and including Japan, respectively; Fig EV4). Still, neither the lockdown strictness nor the lockdown duration was significantly correlated with the log Aug-20 COVID-19 Mortality Probability (Table EV3). The significant correlation sustained when the log Aug-20 COVID-19 Mortality Probability was examined across the 58 countries for which data are available, across the United States countries, and when the log Aug-20 COVID-19 Infection Probability was examined (Appendix Figs S3–S5). Altogether, these analyses imply that the initial response was critical to curb total COVID-19-related mortality and had a long-term impact. Click here to expand this figure. Figure EV4. A semi-logarithmic scatter plot of the Aug-20 COVID-19 Mortality Probability and τ in the OECD countriesThe x-axis represents τ, the difference between the social distancing start time and the day in which the first ten deaths were recorded for the respective country (intuitively, the response time). The y-axis represents the Aug-20 COVID-19 Mortality Probability in a logarithmic scale. Dot sizes are proportional to population sizes. Pearson r2 = 0.34 (P value = 2e-4) when including Japan and r2 = 0.62 (P value = 1e-8) when excluding Japan. The dashed line corresponds to the fitted regression, excluding Japan: log (Aug–20 COVID 19 Mortality Probability) = 0.039τ–1.26. Download figure Download PowerPoint Discussion In this study, we modeled the mobility dynamics across time during the COVID-19 pandemic. Using this model, we computed explanatory features that characterize a lockdown, and in turn, these features provided a quantitative measure for comparing the lockdown dynamics and outcome across countries. We found high correlation between the response time of a country and its mortality rate. This finding suggests that countries that took early measures to limit population mixing had better control on the viral-related mortality. While these conclusions were derived for the lockdown period, i.e., in the midst of the pandemic when the mortality rates could roughly be predicted, accumulation of more recent data demonstrates that the initial lockdown response time has a prolonged impact on mortality rates. In contrast, neither the lockdown duration nor the lockdown strictness was significantly correlated with the mortality rates (Tables EV1 and EV3). These results imply that a tight lockdown has been unnecessary and that the immediate response was of utmost importance. Mobility data collected from location identification of various smartphone applications have been previously analyzed in relation with the COVID-19 pandemic, e.g., to better understand the importance of travel restrictions on the infection rate or to construct platforms for capturing movements between provinces for decision making (Badr et al, 2020; Benzell et al, 2020; Bonaccorsi et al, 2020; Kraemer et al, 2020; Linka et al, 2020; Pepe et al, 2020; preprint: Soures et al, 2020). All of these studies proved that c" @default.
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• W3092931661 cites W3002539152 @default.
• W3092931661 cites W3003573988 @default.
• W3092931661 cites W3003668884 @default.
• W3092931661 cites W3008443627 @default.
• W3092931661 cites W3013594674 @default.
• W3092931661 cites W3015467202 @default.
• W3092931661 cites W3016385182 @default.
• W3092931661 cites W3017203823 @default.
• W3092931661 cites W3019620648 @default.
• W3092931661 cites W3019643748 @default.
• W3092931661 cites W3022970339 @default.
• W3092931661 cites W3023292213 @default.
• W3092931661 cites W3023384655 @default.
• W3092931661 cites W3023863810 @default.
• W3092931661 cites W3026903838 @default.
• W3092931661 cites W3028878294 @default.
• W3092931661 cites W3032971139 @default.
• W3092931661 cites W3036937686 @default.
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