Mathematical models for predicting human mobility in the context of infectious disease spread: introducing the impedance model.

BACKGROUND: Mathematical models of human mobility have demonstrated a great potential for infectious disease epidemiology in contexts of data scarcity. While the commonly used gravity model involves parameter tuning and is thus difficult to implement without reference data, the more recent radiation model based on population densities is parameter-free, but biased. In this study we introduce the new impedance model, by analogy with electricity. Previous research has compared models on the basis of a few specific available spatial patterns. In this study, we use a systematic simulation-based approach to assess the performances. METHODS: Five hundred spatial patterns were generated using various area sizes and location coordinates. Model performances were evaluated based on these patterns. For simulated data, comparison measures were average root mean square error (aRMSE) and bias criteria. Modeling of the 2010 Haiti cholera epidemic with a basic susceptible-infected-recovered (SIR) framework allowed an empirical evaluation through assessing the goodness-of-fit of the observed epidemic curve. RESULTS: The new, parameter-free impedance model outperformed previous models on simulated data according to average aRMSE and bias criteria. The impedance model achieved better performances with heterogeneous population densities and small destination populations. As a proof of concept, the basic compartmental SIR framework was used to confirm the results obtained with the impedance model in predicting the spread of cholera in Haiti in 2010. CONCLUSIONS: The proposed new impedance model provides accurate estimations of human mobility, especially when the population distribution is highly heterogeneous. This model can therefore help to achieve more accurate predictions of disease spread in the context of an epidemic.

Census-derived migration data as a tool for informing malaria elimination policy.

BACKGROUND: Numerous countries around the world are approaching malaria elimination. Until global eradication is achieved, countries that successfully eliminate the disease will contend with parasite reintroduction through international movement of infected people. Human-mediated parasite mobility is also important within countries near elimination, as it drives parasite flows that affect disease transmission on a subnational scale. METHODS: Movement patterns exhibited in census-based migration data are compared with patterns exhibited in a mobile phone data set from Haiti to quantify how well migration data predict short-term movement patterns. Because short-term movement data were unavailable for Mesoamerica, a logistic regression model fit to migration data from three countries in Mesoamerica is used to predict flows of infected people between subnational administrative units throughout the region. RESULTS: Population flows predicted using census-based migration data correlated strongly with mobile phone-derived movements when used as a measure of relative connectivity. Relative population flows are therefore predicted using census data across Mesoamerica, informing the areas that are likely exporters and importers of infected people. Relative population flows are used to identify community structure, useful for coordinating interventions and elimination efforts to minimize importation risk. Finally, the ability of census microdata inform future intervention planning is discussed in a country-specific setting using Costa Rica as an example. CONCLUSIONS: These results show long-term migration data can effectively predict the relative flows of infected people to direct malaria elimination policy, a particularly relevant result because migration data are generally easier to obtain than short-term movement data such as mobile phone records. Further, predicted relative flows highlight policy-relevant population dynamics, such as major exporters across the region, and Nicaragua and Costa Rica's strong connection by movement of infected people, suggesting close coordination of their elimination efforts. Country-specific applications are discussed as well, such as predicting areas at relatively high risk of importation, which could inform surveillance and treatment strategies.

Predicting attitudinal and behavioral responses to COVID-19 pandemic using machine learning.

At the beginning of 2020, COVID-19 became a global problem. Despite all the efforts to emphasize the relevance of preventive measures, not everyone adhered to them. Thus, learning more about the characteristics determining attitudinal and behavioral responses to the pandemic is crucial to improving future interventions. In this study, we applied machine learning on the multinational data collected by the International Collaboration on the Social and Moral Psychology of COVID-19 (N = 51,404) to test the predictive efficacy of constructs from social, moral, cognitive, and personality psychology, as well as socio-demographic factors, in the attitudinal and behavioral responses to the pandemic. The results point to several valuable insights. Internalized moral identity provided the most consistent predictive contribution-individuals perceiving moral traits as central to their self-concept reported higher adherence to preventive measures. Similar results were found for morality as cooperation, symbolized moral identity, self-control, open-mindedness, and collective narcissism, while the inverse relationship was evident for the endorsement of conspiracy theories. However, we also found a non-neglible variability in the explained variance and predictive contributions with respect to macro-level factors such as the pandemic stage or cultural region. Overall, the results underscore the importance of morality-related and contextual factors in understanding adherence to public health recommendations during the pandemic.

Evaluation of a Schanz pin technique for posterior cruciate ligament tensioning during reconstruction.

Maximum anterior positioning of the tibia relative to the femur during posterior cruciate ligament (PCL) reconstruction is essential for achieving a tight graft and stable joint. A Schanz pin inserted in the proximal tibia is sometimes used to pull the proximal tibia forward during tensioning of the graft in PCL reconstruction. This study was designed to evaluate whether this technique provides more anterior translation than the traditional anterior drawer technique. Eight fresh-frozen cadaveric knees were tested using both methods in randomized order: pulling anteriorly on a 5-mm Schanz pin in the proximal tibia or a leather strap behind the calf designed to simulate a surgeon's hand performing an anterior drawer maneuver. An anteriorly directed force was applied from 0 to 60 N, and the sagittal position of the tibia in relation to the femur was recorded using a mini C-arm. Tests were performed first on the intact knees, again after the PCL had been cut, and again following transection of the popliteal-fibular ligament. We found a statistically significant (p < 0.05) increase in tibial translation, ranging between 1 and 2 mm, when the tibia was pulled by the Schanz pin compared with the strap under every set of conditions. This greater anterior translation could improve the stability of the postreconstructed knee.

Publisher Correction: Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus.

This Article was mistakenly not made Open Access when originally published; this has now been amended, and information about the Creative Commons Attribution 4.0 International License has been added into the 'Additional information' section.

Publisher Correction: Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus.

In the version of this Article originally published, the affiliation for author Catherine Linard was incorrectly stated as '6Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK'. The correct affiliation is '9Spatial Epidemiology Lab (SpELL), Universite Libre de Bruxelles, Brussels, Belgium'. The affiliation for author Hongjie Yu was also incorrectly stated as '11Department of Statistics, Harvard University, Cambridge, MA, USA'. The correct affiliation is '15School of Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China'. This has now been amended in all versions of the Article.

Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus.

The global population at risk from mosquito-borne diseases-including dengue, yellow fever, chikungunya and Zika-is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.

Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data.

Background: Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New 'big data' approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures. Methods: We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national 'stay at home' lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis. Results: Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15-30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted. Conclusions: The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.

Mapping poverty using mobile phone and satellite data.

Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.

Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen.

Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm's landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p < 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people's preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.

Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake.

INTRODUCTION: Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated. METHODS: A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users. RESULTS: Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal. DISCUSSION: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies.

Remotely measuring populations during a crisis by overlaying two data sources.

BACKGROUND: Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods. METHODS: We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales. RESULTS: The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis. CONCLUSIONS: Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.

Using mobile phone data to predict the spatial spread of cholera.

Effective response to infectious disease epidemics requires focused control measures in areas predicted to be at high risk of new outbreaks. We aimed to test whether mobile operator data could predict the early spatial evolution of the 2010 Haiti cholera epidemic. Daily case data were analysed for 78 study areas from October 16 to December 16, 2010. Movements of 2.9 million anonymous mobile phone SIM cards were used to create a national mobility network. Two gravity models of population mobility were implemented for comparison. Both were optimized based on the complete retrospective epidemic data, available only after the end of the epidemic spread. Risk of an area experiencing an outbreak within seven days showed strong dose-response relationship with the mobile phone-based infectious pressure estimates. The mobile phone-based model performed better (AUC 0.79) than the retrospectively optimized gravity models (AUC 0.66 and 0.74, respectively). Infectious pressure at outbreak onset was significantly correlated with reported cholera cases during the first ten days of the epidemic (p < 0.05). Mobile operator data is a highly promising data source for improving preparedness and response efforts during cholera outbreaks. Findings may be particularly important for containment efforts of emerging infectious diseases, including high-mortality influenza strains.

Approaching the limit of predictability in human mobility.

In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors, and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.