Title: Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model
AbstractBackground
More details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar.
Methods
An agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation.
Results
Mobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups.
Conclusions
Using an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.
Estimating malaria risk associated with work locations and travel across a region provides local health officials with information useful to mitigate possible transmission paths of malaria as well as understand the risk of exposure for local populations. This study investigates malaria exposure risk by analysing the spatial pattern of malaria cases (primarilyPlasmodium vivax)in Ubon Ratchathani and Sisaket provinces of Thailand, using an ecological niche model and machine learning to estimate the species distribution ofP. vivaxmalaria and compare the resulting niche areas with occupation type, work locations, and work-related travel routes.
Methods
A maximum entropy model was trained to estimate the distribution ofP. vivaxmalaria for a period between January 2019 and April 2020, capturing estimated malaria occurrence for these provinces. A random simulation workflow was developed to make region-based case data usable for the machine learning approach. This workflow was used to generate a probability surface for the ecological niche regions. The resulting niche regions were analysed by occupation type, home and work locations, and work-related travel routes to determine the relationship between these variables and malaria occurrence. A one-way analysis of variance (ANOVA) test was used to understand the relationship between predicted malaria occurrence and occupation type.
Results
The MaxEnt (full name) model indicated a higher occurrence ofP. vivaxmalaria in forested areas especially along the Thailand–Cambodia border. The ANOVA results showed a statistically significant difference between average malaria risk values predicted from the ecological niche model for rubber plantation workers and farmers, the two main occupation groups in the study. The rubber plantation workers were found to be at higher risk of exposure to malaria than farmers in Ubon Ratchathani and Sisaket provinces of Thailand.
Conclusion
The results from this study point to occupation-related factors such as work location and the routes travelled to work, being risk factors in malaria occurrence and possible contributors to transmission among local populations.
Li, Rui; Chester, Mikhail V.; Middel, Ariane; Vanos, Jennifer K.; Hernandez-Cortes, Danae; Buo, Isaac; Hondula, David M.(
, Frontiers in Sustainable Cities)
Urban heat exposure is an increasing health risk among urban dwellers. Many cities are considering accommodating active mobility, especially walking and biking, to reduce greenhouse gas emissions. However, promoting active mobility without proper planning and transportation infrastructure to combat extreme heat exposure may cause more heat-related morbidity and mortality, particularly in future with projected climate change. This study estimated the effectiveness of active trip heat exposure mitigation under built environment and travel behavior change. Simulations of the Phoenix metro region's 624,987 active trips were conducted using the activity-based travel model (ABM), mean radiant temperature (T MRT , net human radiation exposure), transportation network, and local climate zones. Two scenarios were designed to reduce traveler exposure: one that focuses on built environment change (making neighborhoods cooler) and the other on travel behavior (switching from shorter travel time but higher exposure routes to longer travel time but cooler routes) change. Travelers experienced T MRT heat exposure ranging from 29°C to 76°C (84°F to 168°F) without environmental or behavioral change. Active trip T MRT exposures were reduced by an average of 1.2–3.7°C when the built environment was changed from a hotter to cooler design. Behavioral changes cooled up to 10 times more trips than changes in built environment changes. The marginal benefit of cooling decreased as the number of cooled corridors transformed increased. When the most traveled 10 km of corridors were cooled, the marginal benefit affected over 1,000 trips/km. However, cooling all corridors results in marginal benefits as low as 1 trip/km. The results reveal that heavily traveled corridors should be prioritized with limited resources, and the best cooling results come from environment and travel behavior change together. The results show how to surgically invest in travel behavior and built environment change to most effectively protect active travelers.
Zhu, G.; Stewart, K.; Niemeier, D.; Fan, J.(
, ISPRS international journal of geoinformation)
Kainz, W.; Manley, E.; Delmelle, E.; Birkin, M.; Gahegan, M.; Kwan, M-P.
(Ed.)
As of March 2021, the State of Florida, U.S.A. had accounted for approximately 6.67% of total COVID-19 (SARS-CoV-2 coronavirus disease) cases in the U.S. The main objective of this research is to analyze mobility patterns during a three month period in summer 2020, when COVID-19 case numbers were very high for three Florida counties, Miami-Dade, Broward, and Palm Beach counties.
To investigate patterns, as well as drivers, related to changes in mobility across the tri-county region, a random forest regression model was built using sociodemographic, travel, and built environment factors, as well as COVID-19 positive case data. Mobility patterns declined in each county when new
COVID-19 infections began to rise, beginning in mid-June 2020. While the mean number of bar and restaurant visits was lower overall due to closures, analysis showed that these visits remained a top factor that impacted mobility for all three counties, even with a rise in cases. Our modeling results
suggest that there were mobility pattern differences between counties with respect to factors relating, for example, to race and ethnicity (different population groups factored differently in each county),as well as social distancing or travel-related factors (e.g., staying at home behaviors) over the two time periods prior to and after the spike of COVID-19 cases.
Lamb, Matthew R.; Kandula, Sasikiran; Shaman, Jeffrey(
, Influenza and Other Respiratory Viruses)
AbstractBackground
New York City (NYC) has been one of the hotspots of the COVID‐19 pandemic in the United States. By the end of April 2020, close to 165 000 cases and 13 000 deaths were reported in the city with considerable variability across the city's ZIP codes.
Objectives
In this study, we examine: (a) the extent to which the variability in ZIP code‐level case positivity can be explained by aggregate markers of socioeconomic status (SES) and daily change in mobility; and (b) the extent to which daily change in mobility independently predicts case positivity.
Methods
COVID‐19 case positivity by ZIP code was modeled using multivariable linear regression with generalized estimating equations to account for within‐ZIP clustering. Daily case positivity was obtained from NYC Department of Health and Mental Hygiene and measures of SES were based on data from the American Community Survey. Changes in human mobility were estimated using anonymized aggregated mobile phone location systems.
Results
Our analysis indicates that the socioeconomic markers considered together explained 56% of the variability in case positivity through April 1 and their explanatory power decreased to 18% by April 30. Changes in mobility during this time period are not likely to be acting as a mediator of the relationship between ZIP‐level SES and case positivity. During the middle of April, increases in mobility were independently associated with decreased case positivity.
Conclusions
Together, these findings present evidence that heterogeneity in COVID‐19 case positivity during NYC’s spring outbreak was largely driven by residents’ SES.
Nugent, Selin E.(
, American Journal of Physical Anthropology)
AbstractObjectives
This article explores the scale and seasonal patterns of mobility at the complex settlement site of Qızqala during the Middle Bronze Age (2400–1,500 BCE). By integrating human bone, teeth, and environmental samples this research tests the hypothesis of the persistent importance of community‐wide seasonal pastoral transhumance during the early formation of complex settlement systems of the South Caucasus.
Methods
This research applies stable oxygen and radiogenic strontium isotope analyses on incremental samples of human tooth enamel, bulk tooth enamel, and bone to resolve mobility patterns. Sequential and bulk sampling techniques elucidate seasonal and residential mobility behaviors. Extensive environmental isotope samples of plant and water were collected through regional survey and establish local and regional isotopic baselines, which are compared to human isotope analysis results.
Results
Qızqala individuals exhibit low isotopic variability compared to regional contemporaries.87Sr/86Sr ratios from human remains indicate seasonal and residential isotopic variability within the baseline ranges of local landscapes. δ18O values display erratic patterns, but correspond to seasonal variability with fluctuations between highland and lowland altitudinal zone baseline values.
Conclusions
Results suggest that isotopic analysis of multiple elements and sequential enamel samples offers finer resolution on the complexities of human mobility strategies and elucidate the daily lives of often overlooked mobile populations. Higher resolution of individual mobility reveals shared routine behaviors that underscore the importance of diverse social collaborations in forming complex polities in the South Caucasus.
Li, Yao, Stewart, Kathleen, Han, Kay Thwe, Han, Zay Yar, Aung, Poe P., Thein, Zaw W., Htay, Thura, Chen, Dong, Nyunt, Myaing M., and Plowe, Christopher V. Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model. Clinical Infectious Diseases 76.3 Web. doi:10.1093/cid/ciac568.
Li, Yao, Stewart, Kathleen, Han, Kay Thwe, Han, Zay Yar, Aung, Poe P., Thein, Zaw W., Htay, Thura, Chen, Dong, Nyunt, Myaing M., & Plowe, Christopher V. Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model. Clinical Infectious Diseases, 76 (3). https://doi.org/10.1093/cid/ciac568
Li, Yao, Stewart, Kathleen, Han, Kay Thwe, Han, Zay Yar, Aung, Poe P., Thein, Zaw W., Htay, Thura, Chen, Dong, Nyunt, Myaing M., and Plowe, Christopher V.
"Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model". Clinical Infectious Diseases 76 (3). Country unknown/Code not available: Oxford University Press. https://doi.org/10.1093/cid/ciac568.https://par.nsf.gov/biblio/10417544.
@article{osti_10417544,
place = {Country unknown/Code not available},
title = {Understanding Spatiotemporal Human Mobility Patterns for Malaria Control Using a Multiagent Mobility Simulation Model},
url = {https://par.nsf.gov/biblio/10417544},
DOI = {10.1093/cid/ciac568},
abstractNote = {Abstract BackgroundMore details about human movement patterns are needed to evaluate relationships between daily travel and malaria risk at finer scales. A multiagent mobility simulation model was built to simulate the movements of villagers between home and their workplaces in 2 townships in Myanmar. MethodsAn agent-based model (ABM) was built to simulate daily travel to and from work based on responses to a travel survey. Key elements for the ABM were land cover, travel time, travel mode, occupation, malaria prevalence, and a detailed road network. Most visited network segments for different occupations and for malaria-positive cases were extracted and compared. Data from a separate survey were used to validate the simulation. ResultsMobility characteristics for different occupation groups showed that while certain patterns were shared among some groups, there were also patterns that were unique to an occupation group. Forest workers were estimated to be the most mobile occupation group, and also had the highest potential malaria exposure associated with their daily travel in Ann Township. In Singu Township, forest workers were not the most mobile group; however, they were estimated to visit regions that had higher prevalence of malaria infection over other occupation groups. ConclusionsUsing an ABM to simulate daily travel generated mobility patterns for different occupation groups. These spatial patterns varied by occupation. Our simulation identified occupations at a higher risk of being exposed to malaria and where these exposures were more likely to occur.},
journal = {Clinical Infectious Diseases},
volume = {76},
number = {3},
publisher = {Oxford University Press},
author = {Li, Yao and Stewart, Kathleen and Han, Kay Thwe and Han, Zay Yar and Aung, Poe P. and Thein, Zaw W. and Htay, Thura and Chen, Dong and Nyunt, Myaing M. and Plowe, Christopher V.},
}
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