Human Mobility Restrictions and COVID-19 Infection Rates: Analysis of Mobility Data and Coronavirus Spread in Poland and Portugal

Strzelecki, Artur; Azevedo, Ana; Rizun, Mariia; Rutecka, Paulina; Zagała, Kacper; Cicha, Karina; Albuquerque, Alexandra

doi:10.3390/ijerph192114455

Cited by 2 publications

(3 citation statements)

References 57 publications

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“…We have included wage losses for proximate contacts in our indirect costs estimates, as shown in Equation (3). It is important to isolate or quarantine proximate contacts of identified cases to prevent the infection's secondary spread.…”

Section: Economic Impact Analysismentioning

confidence: 99%

“…Furthermore, they allowed governments sufficient time to build adequate healthcare infrastructure. Lockdowns restricted human mobility [3,4], and, thus, the spread of the virus. The global evidence suggests that strict mobility restrictions reduced COVID-19 cases and COVID-19-related deaths [5][6][7][8] in high-income [9][10][11] as well as low-and-middle-income countries (LMICs) [12].…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

2023

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Introduction: Maharashtra, India, remained a hotspot during the COVID-19 pandemic. After the initial complete lockdown, the state slowly relaxed restrictions. We aim to estimate the lockdown’s impact on COVID-19 cases and associated healthcare costs. Methods: Using daily case data for 84 days (9 March–31 May 2020), we modeled the epidemic’s trajectory and predicted new cases for different phases of lockdown. We fitted log-linear models to estimate the growth rate, basic (R0), daily reproduction number (Re), and case doubling time. Based on pre-restriction and Phase 1 R0, we predicted new cases for the rest of the restriction phases, and we compared them with the actual number of cases during each phase. Furthermore, using the published and gray literature, we estimated the costs and savings of implementing these restrictions for the projected period, and we performed a sensitivity analysis. Results: The estimated median R0 during the different phases was 1.14 (95% CI: 0.85, 1.45) for pre-lockdown, 1.67 (95% CI: 1.50, 1.82) for phase 1 (strict mobility restrictions), 1.24 (95% CI: 1.12, 1.35) for phase 2 (extension of phase 1 with no restrictions on agricultural and essential services), 1.12 (95% CI: 1.01, 1.23) for phase 3 (extension of phase 2 with mobility relaxations in areas with few infections), and 1.05 (95% CI: 0.99, 1.123) for phase 4 (implementation of localized lockdowns in high-case-load areas with fewer restrictions on other areas), respectively. The corresponding doubling time rate for cases (in days) was 17.78 (95% CI: 5.61, −15.19), 3.87 (95% CI: 3.15, 5.00), 10.37 (95% CI: 7.10, 19.30), 20.31 (95% CI: 10.70, 212.50), and 45.56 (95% CI: 20.50, –204.52). For the projected period, the cases could have reached 631,819 without the lockdown, as the actual reported number of cases was 64,975. From a healthcare perspective, the estimated total value of averted cases was INR 194.73 billion (USD 2.60 billion), resulting in net cost savings of 84.05%. The Incremental Cost-Effectiveness Ratio (ICER) per Quality Adjusted Life Year (QALY) for implementing the lockdown, rather than observing the natural course of the pandemic, was INR 33,812.15 (USD 450.83). Conclusion: Maharashtra’s early public health response delayed the pandemic and averted new cases and deaths during the first wave of the pandemic. However, we recommend that such restrictions be carefully used while considering the local socio-economic realities in countries like India.

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Section: Economic Impact Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

2023

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“…Several ICT companies, such as Google and Apple, share reports on the mobility of the population, using new technologies that are an integral part of their products or services, daily [ 19 ]. They provide movement data from smartphones running mobile applications such as Google Maps or Apple Maps to identify changes in people’s mobility due to COVID-19 [ 20 ]. These data cover a large proportion of the population.…”

Section: Introductionmentioning

confidence: 99%

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

Strzelecki

2022

Healthcare

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The objective of this systematic review with PRISMA guidelines is to discover how population movement information has epidemiological implications for the spread of COVID-19. In November 2022, the Web of Science and Scopus databases were searched for relevant reports for the review. The inclusion criteria are: (1) the study uses data from Apple Mobility Trends Reports, (2) the context of the study is about COVID-19 mobility patterns, and (3) the report is published in a peer-reviewed venue in the form of an article or conference paper in English. The review included 35 studies in the period of 2020–2022. The main strategy used for data extraction in this review is a matrix proposal to present each study from a perspective of research objective and outcome, study context, country, time span, and conducted research method. We conclude by pointing out that these data are not often used in studies and it is better to study a single country instead of doing multiple-country research. We propose topic classifications for the context of the studies as transmission rate, transport policy, air quality, re-increased activities, economic activities, and financial markets.

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Human Mobility Restrictions and COVID-19 Infection Rates: Analysis of Mobility Data and Coronavirus Spread in Poland and Portugal

Cited by 2 publications

References 57 publications

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

COVID-19 Pandemic: Did Strict Mobility Restrictions Save Lives and Healthcare Costs in Maharashtra, India?

The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis

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