Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data

Infectious disease dynamics are affected by human mobility more powerfully than previously thought, and thus reliable traceability data are essential. In rural riverine settings, lack of infrastructure and dense tree coverage deter the implementation of cutting-edge technology to collect human mobility data. To overcome this challenge, this study proposed the use of a novel open mobile mapping tool, GeoODK. This study consists of a purposive sampling of 33 participants in six villages with contrasting patterns of malaria transmission that demonstrates a feasible approach to map human mobility. The self-reported traceability data allowed the construction of the first human mobility framework in rural riverine villages in the Peruvian Amazon. The mobility spectrum in these areas resulted in travel profiles ranging from 2 hours to 19 days; and distances between 10 to 167 km. Most Importantly, occupational-related mobility profiles with the highest displacements (in terms of time and distance) were observed in commercial, logging, and hunting activities. These data are consistent with malaria transmission studies in the area that show villages in watersheds with higher human movement are concurrently those with greater malaria risk. The approach we describe represents a potential tool to gather critical information that can facilitate malaria control activities.

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Observing the silent world under COVID-19 with a comprehensive impact analysis based on human mobility

Scientific Reports ◽

10.1038/s41598-021-94060-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shaobin Wang ◽

Yun Tong ◽

Yupeng Fan ◽

Haimeng Liu ◽

Jun Wu ◽

...

Keyword(s):

Impact Analysis ◽

Human Mobility ◽

Economic Activities ◽

Underdeveloped Countries ◽

Mobility Data ◽

Long Run ◽

Level Of Economic Development ◽

Human World ◽

Dynamic Relationships ◽

The Impact

AbstractSince spring 2020, the human world seems to be exceptionally silent due to mobility reduction caused by the COVID-19 pandemic. To better measure the real-time decline of human mobility and changes in socio-economic activities in a timely manner, we constructed a silent index (SI) based on Google’s mobility data. We systematically investigated the relations between SI, new COVID-19 cases, government policy, and the level of economic development. Results showed a drastic impact of the COVID-19 pandemic on increasing SI. The impact of COVID-19 on human mobility varied significantly by country and place. Bi-directional dynamic relationships between SI and the new COVID-19 cases were detected, with a lagging period of one to two weeks. The travel restriction and social policies could immediately affect SI in one week; however, could not effectively sustain in the long run. SI may reflect the disturbing impact of disasters or catastrophic events on the activities related to the global or national economy. Underdeveloped countries are more affected by the COVID-19 pandemic.

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Meteorological and human mobility data on predicting COVID-19 cases by a novel hybrid decomposition method with anomaly detection analysis: a case study in the capitals of Brazil

Expert Systems with Applications ◽

10.1016/j.eswa.2021.115190 ◽

2021 ◽

pp. 115190

Author(s):

Tiago Tiburcio da Silva ◽

Rodrigo Francisquini ◽

Maric C. V. Nascimento

Keyword(s):

Anomaly Detection ◽

Decomposition Method ◽

Human Mobility ◽

Mobility Data ◽

Detection Analysis ◽

Hybrid Decomposition

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Segregation or integration? Exploring activity disparities between migrants and settled urban residents using human mobility data

Transactions in GIS ◽

10.1111/tgis.12760 ◽

2021 ◽

Author(s):

Qi‐Li Gao ◽

Yang Yue ◽

Wei Tu ◽

Jinzhou Cao ◽

Qing‐Quan Li

Keyword(s):

Human Mobility ◽

Urban Residents ◽

Mobility Data

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Mobility Patterns in Different Age Groups in Japan during the COVID-19 Pandemic: a Small Area Time Series Analysis through March 2021

Journal of Urban Health ◽

10.1007/s11524-021-00566-7 ◽

2021 ◽

Author(s):

Shuhei Nomura ◽

Yuta Tanoue ◽

Daisuke Yoneoka ◽

Stuart Gilmour ◽

Takayuki Kawashima ◽

...

Keyword(s):

Care Delivery ◽

Human Mobility ◽

Age Groups ◽

Virus Transmission ◽

Initial Response ◽

Health Care Delivery System ◽

Population Characteristics ◽

Mobility Patterns ◽

Mobility Data ◽

The Status

AbstractIn the COVID-19 era, movement restrictions are crucial to slow virus transmission and have been implemented in most parts of the world, including Japan. To find new insights on human mobility and movement restrictions encouraged (but not forced) by the emergency declaration in Japan, we analyzed mobility data at 35 major stations and downtown areas in Japan—each defined as an area overlaid by several 125-meter grids—from September 1, 2019 to March 19, 2021. Data on the total number of unique individuals per hour passing through each area were obtained from Yahoo Japan Corporation (i.e., more than 13,500 data points for each area). We examined the temporal trend in the ratio of the rolling seven-day daily average of the total population to a baseline on January 16, 2020, by ten-year age groups in five time frames. We demonstrated that the degree and trend of mobility decline after the declaration of a state of emergency varies across age groups and even at the subregional level. We demonstrated that monitoring dynamic geographic and temporal mobility information stratified by detailed population characteristics can help guide not only exit strategies from an ongoing emergency declaration, but also initial response strategies before the next possible resurgence. Combining such detailed data with data on vaccination coverage and COVID-19 incidence (including the status of the health care delivery system) can help governments and local authorities develop community-specific mobility restriction policies. This could include strengthening incentives to stay home and raising awareness of cognitive errors that weaken people's resolve to refrain from nonessential movement.

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Comparative Analysis of Geolocation Information through Mobile-Devices under Different COVID-19 Mobility Restriction Patterns in Spain

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10020073 ◽

2021 ◽

Vol 10 (2) ◽

pp. 73

Author(s):

Raquel Pérez-Arnal ◽

David Conesa ◽

Sergio Alvarez-Napagao ◽

Toyotaro Suzumura ◽

Martí Català ◽

...

Keyword(s):

Mobile Phones ◽

Human Mobility ◽

The Other ◽

Data Sources ◽

Experimental Setting ◽

Mobile Data ◽

Restriction Patterns ◽

Mobility Data ◽

The World ◽

Proper Interpretation

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.

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