scholarly journals Using Mobile Phone Data to Estimate the Relationship between Population Flow and Influenza Infection Pathways

2021 ◽  
Vol 18 (14) ◽  
pp. 7439
Author(s):  
Qiushi Chen ◽  
Michiko Tsubaki ◽  
Yasuhiro Minami ◽  
Kazutoshi Fujibayashi ◽  
Tetsuro Yumoto ◽  
...  
Keyword(s):  
Graphical Models ◽  
Influenza Infection ◽  
Time Lag ◽  
Mobile Phone Data ◽  
Location Data ◽  
Estimated Parameters ◽  
Global Positioning ◽  
Infection Pathways ◽  
The Relationship ◽  
Observation Period

This study aimed to analyze population flow using global positioning system (GPS) location data and evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. Neural collective graphical models (NCGMs; Iwata and Shimizu 2019) were applied for 25 cell areas, each measuring 10 × 10 km2, in Osaka, Kyoto, Nara, and Hyogo prefectures to estimate population flow. An NCGM uses a neural network to incorporate the spatiotemporal dependency issue and reduce the estimated parameters. The prescription peaks between several cells with high population flow showed a high correlation with a delay of one to two days or with a seven-day time-lag. It was observed that not much population flows from one cell to the outside area on weekdays. This observation may have been due to geographical features and undeveloped transportation networks. The number of prescriptions for anti-influenza drugs in that cell remained low during the observation period. The present results indicate that influenza did not spread to areas with undeveloped traffic networks, and the peak number of drug prescriptions arrived with a time lag of several days in areas with a high amount of area-to-area movement due to commuting.

Research on the Relationships Between Population Flow Based on Global Positioning System Location Information from Mobile Phone Networks and Influenza Infection Pathways Based on the Number of Anti-Influenza Drug Prescriptions at Pharmacies: A Pilot Study (Preprint)

2020 ◽  
Author(s):  
Qiushi Chen ◽  
Michiko Tsubaki ◽  
Yasuhiro Minami ◽  
Kazutoshi Fujibayashi ◽  
Tetsuro Yumoto ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Seasonal Influenza ◽  
Influenza Infection ◽  
Time Lag ◽  
Location Information ◽  
Positioning System ◽  
Location Data ◽  
Global Positioning ◽  
Infection Pathways ◽  
The Relationship

BACKGROUND Global seasonal influenza-associated respiratory excess mortality rates have been estimated at 4-8.8 per 100,000 individuals, and this is one of the major issues in public health. Designing efficient containment strategies for highly contagious diseases like influenza has been a subject of very considerable interest recently. Infectious disease epidemic tracking and forecasting have recently been attempted using data based on mobile phone global positioning system (GPS) location information. Tracking and forecasting local influenza spread may contribute to the control of influenza epidemics in an early stage. OBJECTIVE The objectives of this research were to analyze population flow using GPS location data based on the methods proposed by Iwata and Shimizu (2019), and to evaluate influenza infection pathways by determining the relationship between population flow and the number of drugs sold at pharmacies. METHODS Methods proposed by Iwata and Shimizu were applied for all 25 cells to estimate population flow. They proposed a neural collective graphical model (NCGM), which uses a neural network to incorporate the spatiotemporal dependency issue and reduce the estimated parameter. RESULTS The prescription peaks in cells 12 and 14, which had high population flows with cell 13, showed a high correlation with a delay of one to two days. The incubation period is one to four days (average two days) in seasonal influenza. One feature around cell 6 is the low number of prescriptions for anti-influenza drugs. The influenza infection may not have spread to cell 6 due to the low population flow from cells 12 and 13 with high prescriptions. Another feature is the observation of transmission of infection by a small number of influenza patients. In cells 5 and 6 where high population flows were suspected, there was a high cross-correlation value of prescription numbers with a seven-day time-lag. The time-lag is longer than the time-lag observed around cell 13 above. It was observed that not much population flows from cell 19 to the outside area on weekdays. This observation may have been due to geographical features and undeveloped transportation networks. The number of prescriptions for anti-influenza drugs in cell 19 remained low during the observation period. CONCLUSIONS This study conducted population flow estimation analyses during commuting times, based on region-specific GPS location data in four Prefectures in the Kansai region of Japan using methods proposed by Iwata and Shimizu. Furthermore, detailed comparative analyses of the relationship between estimated results of population flow and anti-influenza drug prescription data from pharmacies were conducted. It was found that influenza did not spread to areas with undeveloped traffic networks, and the peak number of drug prescriptions arrived with a time lag of several days in areas with a high amount of area-to-area movement due to commuting.

scholarly journals How Users of a Smartphone Weather Application Are Influenced by Animated Announcements Conveying Rainfall Intensity and Electronic Gifts Promoting Rain Evacuation

2019 ◽  
Vol 14 (9) ◽  
pp. 1236-1244
Author(s):  
Hiroko Nakajima ◽  
Kan Shimazaki ◽  
Yang Ishigaki ◽  
Akiko Miyajima ◽  
Akira Kuriyama ◽  
...  
Keyword(s):  
Global Positioning System ◽  
Rainfall Intensity ◽  
Heavy Rain ◽  
Statistical Bias ◽  
Start Time ◽  
Location Data ◽  
Weather Information ◽  
Significant Difference ◽  
Global Positioning ◽  
The Relationship

In this study, we assumed that animated announcements that conveyed rainfall intensity of localized heavy rain and the distribution of electronic gifts to encourage rain evacuation would promote evacuation actions. If evacuation actions could be promoted through these methods, then the transmission of weather information could be improved. Therefore, we modified the features of a weather information application for smartphones, which was already widely used, and conducted a demonstrative experiment with application users who agreed to participate in order to check the validity. We analyzed users’ behaviors by transmitting information regarding the predicted start time of rain and recording the Global Positioning System coordinates of the users’ smartphones. In addition, a questionnaire survey was administered to the users after the experiment to collect data on their conception of rainfall intensity. The participants were also interviewed. The results of the experiment showed a significant difference in user conception of rainfall intensity depending on whether they had viewed the animation. However, a behavior analysis based on location data showed no statistical bias in the relationship between the animation and rain evacuation behavior.

scholarly journals The effective reproductive number (Rt) of COVID-19 and its relationship with social distancing

2020 ◽  
Author(s):  
José Alexandre Felizola Diniz-Filho ◽  
Lucas Jardim ◽  
Cristiana M. Toscano ◽  
Thiago Fernando Rangel
Keyword(s):  
Time Lag ◽  
Critical Issue ◽  
The State ◽  
Mobile Phone Data ◽  
Reproductive Number ◽  
Epidemiological Models ◽  
Social Distancing ◽  
Isolation Index ◽  
The Mean ◽  
The Relationship

AbstractThe expansion of the new coronavirus disease (COVID-19) triggered a renewed interest in epidemiological models and on how parameters can be estimated from observed data. Here we investigated the relationship between average number of transmissions though time, the reproductive number Rt, and social distancing index as reported by mobile phone data service inloco, for Goiás State, Brazil, between March and June 2020. We calculated Rt values using EpiEstim package in R-plataform for confirmed cases incidence curve. We found a correlation equal to -0.72 between Rt values and isolation index at a time lag of 8 days. This correlation is also significant for half of the cities of the State with more than 90,000 people, including the 3 largest ones (and the 7 cities with significant correlations account for 43% of the population of the State). As the Rt values were paired with center of the moving window of 7 days, the delay matches the mean incubation period of the virus. Our findings reinforce that isolation index can be an effective surrogate for modeling and epidemiological analyses and, more importantly, helpful for anticipating the need for early interventions, a critical issue in public health.

Hemmkörperentwicklung bei Hämophilie-Patienten nach Präparate wechsel

2012 ◽  
Vol 32 (S 01) ◽  
pp. S39-S42 ◽  
Author(s):  
S. Kocher ◽  
G. Asmelash ◽  
V. Makki ◽  
S. Müller ◽  
S. Krekeler ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Observational Study ◽  
Retrospective Observational Study ◽  
Frequent Change ◽  
Patient Age ◽  
Treatment Regimens ◽  
Patient Âgé ◽  
The Relationship ◽  
Observation Period

SummaryThe retrospective observational study surveys the relationship between development of inhibitors in the treatment of haemophilia patients and risk factors such as changing FVIII products. A total of 119 patients were included in this study, 198 changes of FVIII products were evaluated. Results: During the observation period of 12 months none of the patients developed an inhibitor, which was temporally associated with a change of FVIII products. A frequent change of FVIII products didn’t lead to an increase in inhibitor risk. The change between plasmatic and recombinant preparations could not be confirmed as a risk factor. Furthermore, no correlation between treatment regimens, severity, patient age and comorbidities of the patients could be found.

scholarly journals A Time-Based Objective Measure of Exposure to the Food Environment

2019 ◽  
Vol 16 (7) ◽  
pp. 1180 ◽  
Author(s):  
Jason Scully ◽  
Anne Moudon ◽  
Philip Hurvitz ◽  
Anju Aggarwal ◽  
Adam Drewnowski
Keyword(s):  
Food Environment ◽  
Fast Food ◽  
Objective Measure ◽  
Mobility Patterns ◽  
Time Duration ◽  
Positioning Systems ◽  
Global Positioning ◽  
Fast Food Restaurants ◽  
Logistic Regressions ◽  
The Relationship

Exposure to food environments has mainly been limited to counting food outlets near participants’ homes. This study considers food environment exposures in time and space using global positioning systems (GPS) records and fast food restaurants (FFRs) as the environment of interest. Data came from 412 participants (median participant age of 45) in the Seattle Obesity Study II who completed a survey, wore GPS receivers, and filled out travel logs for seven days. FFR locations were obtained from Public Health Seattle King County and geocoded. Exposure was conceptualized as contact between stressors (FFRs) and receptors (participants’ mobility records from GPS data) using four proximities: 21 m, 100 m, 500 m, and ½ mile. Measures included count of proximal FFRs, time duration in proximity to ≥1 FFR, and time duration in proximity to FFRs weighted by FFR counts. Self-reported exposures (FFR visits) were excluded from these measures. Logistic regressions tested associations between one or more reported FFR visits and the three exposure measures at the four proximities. Time spent in proximity to an FFR was associated with significantly higher odds of FFR visits at all proximities. Weighted duration also showed positive associations with FFR visits at 21-m and 100-m proximities. FFR counts were not associated with FFR visits. Duration of exposure helps measure the relationship between the food environment, mobility patterns, and health behaviors. The stronger associations between exposure and outcome found at closer proximities (<100 m) need further research.

scholarly journals Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors

2021 ◽  
Vol 11 (5) ◽  
pp. 328
Author(s):  
Michael Leutner ◽  
Nils Haug ◽  
Luise Bellach ◽  
Elma Dervic ◽  
Alexander Kautzky ◽  
...  
Keyword(s):  
Risk Factors ◽  
Liver Disease ◽  
Diabetic Patients ◽  
Complication Risk ◽  
Increased Risk ◽  
Icd 10 ◽  
Healthy Control ◽  
Design And Methods ◽  
The Relationship ◽  
Observation Period

Objectives: Diabetic patients are often diagnosed with several comorbidities. The aim of the present study was to investigate the relationship between different combinations of risk factors and complications in diabetic patients. Research design and methods: We used a longitudinal, population-wide dataset of patients with hospital diagnoses and identified all patients (n = 195,575) receiving a diagnosis of diabetes in the observation period from 2003–2014. We defined nine ICD-10-codes as risk factors and 16 ICD-10 codes as complications. Using a computational algorithm, cohort patients were assigned to clusters based on the risk factors they were diagnosed with. The clusters were defined so that the patients assigned to them developed similar complications. Complication risk was quantified in terms of relative risk (RR) compared with healthy control patients. Results: We identified five clusters associated with an increased risk of complications. A combined diagnosis of arterial hypertension (aHTN) and dyslipidemia was shared by all clusters and expressed a baseline of increased risk. Additional diagnosis of (1) smoking, (2) depression, (3) liver disease, or (4) obesity made up the other four clusters and further increased the risk of complications. Cluster 9 (aHTN, dyslipidemia and depression) represented diabetic patients at high risk of angina pectoris “AP” (RR: 7.35, CI: 6.74–8.01), kidney disease (RR: 3.18, CI: 3.04–3.32), polyneuropathy (RR: 4.80, CI: 4.23–5.45), and stroke (RR: 4.32, CI: 3.95–4.71), whereas cluster 10 (aHTN, dyslipidemia and smoking) identified patients with the highest risk of AP (RR: 10.10, CI: 9.28–10.98), atherosclerosis (RR: 4.07, CI: 3.84–4.31), and loss of extremities (RR: 4.21, CI: 1.5–11.84) compared to the controls. Conclusions: A comorbidity of aHTN and dyslipidemia was shown to be associated with diabetic complications across all risk-clusters. This effect was amplified by a combination with either depression, smoking, obesity, or non-specific liver disease.

Appendectomy Does Not Increase the Risk of Future Emergence of Parkinson’s Disease: A Meta-analysis

2021 ◽  
pp. 000313482198903
Author(s):  
Mitsuru Ishizuka ◽  
Norisuke Shibuya ◽  
Kazutoshi Takagi ◽  
Hiroyuki Hachiya ◽  
Kazuma Tago ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Random Effects Models ◽  
Significant Difference ◽  
The Cochrane Library ◽  
The Impact ◽  
The Relationship ◽  
Observation Period

Objective To explore the impact of appendectomy history on emergence of Parkinson’s disease (PD). Background Although there are several studies to investigate the relationship between appendectomy history and emergence of PD, the results are still controversial. Methods We performed a comprehensive electronic search of the literature (the Cochrane Library, PubMed, and the Web of Science) up to April 2020 to identify studies that had employed databases allowing comparison of emergence of PD between patients with and those without appendectomy history. To integrate the impact of appendectomy history on emergence of PD, a meta-analysis was performed using random-effects models to calculate the risk ratio (RR) and 95% confidence interval (CI) for the selected studies, and heterogeneity was analyzed using I2 statistics. Results Four studies involving a total of 6 080 710 patients were included in this meta-analysis. Among 1 470 613 patients with appendectomy history, 1845 (.13%) had emergences of PD during the observation period, whereas among 4 610 097 patients without appendectomy history, 6743 (.15%) had emergences of PD during the observation period. These results revealed that patients with appendectomy history and without appendectomy had almost the same emergence of PD (RR, 1.02; 95% CI, .87-1.20; P = .83; I2 = 87%). Conclusion This meta-analysis has demonstrated that there was no significant difference in emergence of PD between patients with and those without appendectomy history.

scholarly journals Monitoring studies should consider temporal variability to reveal relations between cyanobacterial abundance and environmental variables

2015 ◽  
Vol 87 (3) ◽  
pp. 1717-1726 ◽  
Author(s):  
JULIANA WOJCIECHOWSKI ◽  
ANDRÉ A. PADIAL
Keyword(s):  
Temporal Variability ◽  
Environmental Variables ◽  
Environmental Changes ◽  
Environmental Variability ◽  
Time Lag ◽  
Cyanobacteria Blooms ◽  
Environment Variables ◽  
Sampling Points ◽  
The Relationship

One of the main goals of monitoring cyanobacteria blooms in aquatic environments is to reveal the relationship between cyanobacterial abundance and environmental variables. Studies typically correlate data that were simultaneously sampled. However, samplings occur sparsely over time and may not reveal the short-term responses of cyanobacterial abundance to environmental changes. In this study, we tested the hypothesis that stronger cyanobacteria x environment relationships in monitoring are found when the temporal variability of sampling points is incorporated in the statistical analyses. To this end, we investigated relationships between cyanobacteria and seven environmental variables that were sampled twice yearly for three years across 11 reservoirs, and data from an intensive monitoring in one of these reservoirs. Poor correlations were obtained when correlating data simultaneously sampled. In fact, the 'highly recurrent' role of phosphorus in cyanobacteria blooms is not properly observed in all sampling periods. On the other hand, the strongest correlation values for the total phosphorus x cyanobacteria relationship were observed when we used the variation of sampling points. We have also shown that environment variables better explain cyanobacteria when a time lag is considered. We conclude that, in cyanobacteria monitoring, the best approach to reveal determinants of cyanobacteria blooms is to consider environmental variability.

scholarly journals Migration statistics relevant for malaria transmission in Senegal derived from mobile phone data and used in an agent-based migration model

2016 ◽  
Vol 11 (1s) ◽  
Author(s):  
Adrian M. Tompkins ◽  
Nicky McCreesh
Keyword(s):  
Mobile Phone ◽  
Malaria Transmission ◽  
Gravity Model ◽  
Mobile Phone Data ◽  
Agent Based ◽  
Migration Model ◽  
Location Data ◽  
Home Location ◽  
One Year ◽  
Overnight Stays

One year of mobile phone location data from Senegal is analysed to determine the characteristics of journeys that result in an overnight stay, and are thus relevant for malaria transmission. Defining the home location of each person as the place of most frequent calls, it is found that approximately 60% of people who spend nights away from home have regular destinations that are repeatedly visited, although only 10% have 3 or more regular destinations. The number of journeys involving overnight stays peaks at a distance of 50 km, although roughly half of such journeys exceed 100 km. Most visits only involve a stay of one or two nights away from home, with just 4% exceeding one week. A new agent-based migration model is introduced, based on a gravity model adapted to represent overnight journeys. Each agent makes journeys involving overnight stays to either regular or random locations, with journey and destination probabilities taken from the mobile phone dataset. Preliminary simulations show that the agentbased model can approximately reproduce the patterns of migration involving overnight stays.

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