Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones.

Human travel impacts the spread of infectious diseases across spatial and temporal scales, with broad implications for the biological and social sciences. Individual data on travel patterns have been difficult to obtain, particularly in low-income countries. Travel survey data provide detailed demographic information, but sample sizes are often small and travel histories are hard to validate. Mobile phone records can provide vast quantities of spatio-temporal travel data but vary in spatial resolution and explicitly do not include individual information in order to protect the privacy of subscribers. Here we compare and contrast both sources of data over the same time period in a rural area of Kenya. Although both data sets are able to quantify broad travel patterns and distinguish regional differences in travel, each provides different insights that can be combined to form a more detailed picture of travel in low-income settings to understand the spread of infectious diseases

Risk factors for Plasmodium falciparum infection in the Kenyan Highlands: a cohort study.

BACKGROUND: Malaria transmission in African highland areas can be prone to epidemics, with minor fluctuations in temperature or altitude resulting in highly heterogeneous transmission. In the Kenyan Highlands, where malaria prevalence has been increasing, characterising malaria incidence and identifying risk factors for infection is complicated by asymptomatic infection. METHODS: This all-age cohort study, one element of the Malaria Transmission Consortium, involved monthly follow-up of 3155 residents of the Kisii and Rachuonyo South districts during June 2009-June 2010. Participants were tested for malaria using rapid diagnostic testing at every visit, regardless of symptoms. RESULTS: The incidence of Plasmodium falciparum infection was 0.2 cases per person, although infections were clustered within individuals and over time, with the majority of infections detected in the last month of the cohort study. Overall, incidence was higher in the Rachuonyo district and infections were detected most frequently in 5-10-year-olds. The majority of infections were asymptomatic (58%). Travel away from the study area was a notable risk factor for infection. CONCLUSIONS: Identifying risk factors for malaria infection can help to guide targeting of interventions to populations most likely to be exposed to malaria

Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones

Human travel impacts the spread of infectious diseases across spatial and temporal scales, with broad implications for the biological and social sciences. Individual data on travel patterns have been difficult to obtain, particularly in low-income countries. Travel survey data provide detailed demographic information, but sample sizes are often small and travel histories are hard to validate. Mobile phone records can provide vast quantities of spatio-temporal travel data but vary in spatial resolution and explicitly do not include individual information in order to protect the privacy of subscribers. Here we compare and contrast both sources of data over the same time period in a rural area of Kenya. Although both data sets are able to quantify broad travel patterns and distinguish regional differences in travel, each provides different insights that can be combined to form a more detailed picture of travel in low-income settings to understand the spread of infectious diseases

MOESM1 of Use of different transmission metrics to describe malaria epidemiology in the highlands of western Kenya

Additional file 1. Seroconversion rates (SCR) and corresponding 95 % confidence interval (CI) stratified by elevation and mosquito control categories. The table shows the seroconversion rates by elevation and mosquito control category, demonstrating lower exposure to malaria at altitudes above 1530 m and in households with both IRS and ITNs in their households

Reliability of school surveys in estimating geographic variation in malaria transmission in the western Kenyan highlands.

Contains fulltext : 125827.pdf (publisher's version ) (Open Access

Spatial distribution of school study participants, location of the schools, and community catchment area.

<p>Each point represents the compound of a child included in the study. The black crosses indicate the location of each school that was included in the survey. The black circular outline corresponds to the area with a 600m radius around each school and thus represents the community catchment area sampled during the community survey.</p

Age-adjusted seroprevalence in community and school surveys (all children) by transmission intensity.

<p>The age-adjusted community seroconversion curves (solid) and school aged population (dashed lines). The different transmission intensities are represented as: high (red) moderate (green) and low (blue).</p

Prevalence of malaria infection: adjusted school vs. community surveys in 46 clusters by RDT and serology.

<p>Scatter plots are shown with the line of perfect concordance (x=y) and the data’s reduced major axis using total least squares regression. (A) RDT prevalence per cluster in community vs. adjusted prevalence in all school children. (B) RDT prevalence per cluster in community vs. adjusted school prevalence restricting to children residing within 600m from school.</p

Characteristics of the study population – age and distance travelled to school.

<p>(A) A population pyramid showing the age distribution of those sampled in the community survey compared to those sampled during the school survey. (B) Histogram depicting the distance between the school and compound where each child resides. (C) The proportion of children sampled at each school that reside within 600m of the school.</p

Prevalence of malaria infection in school vs. community surveys in 46 clusters by RDT and serology.

<p>Scatter plots are shown with the line of perfect concordance (x=y) and the data’s reduced major axis using total least squares regression. (A) RDT prevalence per cluster in community vs. all school children. (B) RDT prevalence per cluster in community vs. school children residing within 600m from school. (C) Seroprevalence per cluster in community vs. all school children. (D) Seroprevalence per cluster in community vs. school children residing within 600m from school.</p