Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

International audienc

Quantifying age-related rates of social contact using diaries in a rural coastal population of Kenya

Background Improved understanding and quantification of social contact patterns that govern the transmission dynamics of respiratory viral infections has utility in the design of preventative and control measures such as vaccination and social distancing. The objective of this study was to quantify an age-specific matrix of contact rates for a predominantly rural low-income population that would support transmission dynamic modeling of respiratory viruses. Methods and Findings From the population register of the Kilifi Health and Demographic Surveillance System, coastal Kenya, 150 individuals per age group (50 years) exhibited the highest inter-generational contacts. Rural contact rates were higher than semiurban (18.8 vs 15.6, p = 0.002), with rural primary school students having twice as many assortative contacts as their semiurban peers. Conclusions and Significance This is the first age-specific contact matrix to be defined for tropical Sub-Saharan Africa and has utility in age-structured models to assess the potential impact of interventions for directly transmitted respiratory infections

Mean number of contacts per day stratified by gender, age group (years), presence of shadow, season, residence, days of week of 568 diary participants from the Kilifi Health and Demographic Surveillance System, Kenya.

‡<p>95% CI: 95% confidence intervals derived from 2,000 bootstraps.</p>$<p>Season: Dry  =  January, August, December; Wet  =  September – November</p>&<p>Location. Rural: Ngerenya, Roka, Matsangoni; Semiurban: Kilifi Township, Tezo.</p

Contact mixing patterns.

<p>Part A: Distribution of overall number of contacts (with mean shown as a dashed line). Part B: Mean (dashed line) contact rate per person per day, with boxplots showing median (centre line) and interquartile range (IQR) of contact rates per age group per day. Part C: Contact rate surface (heat map) expressing the mean number of contacts between an individual participant in each age group with individuals in each age group. Part D: Population level numbers of contacts per day within and between age groups (estimated from the matrix defined in (C) scaled by the age-specific resident population size).</p

Map of the study area.

<p>The inset shows the location of the KHDSS in relation to the former Kilifi District (part of Kilifi County). The study area locations are conventionally categorised as semiurban (Kilifi Township [denoted A] and Tezo [B]), and rural (Ngerenya [C], Roka [D] and Matsangoni [E]).</p

Age group specific contact rates with 95% CI^‡.

‡<p>Confidence intervals based on 2,000 bootstraps.</p><p>^*Age group in years.</p

Age specific contact matrices.

<p>Mixing patterns for 371 participants in rural areas (Part A) and 197 participants in semiurban areas (Part B). The description of the images, from left to right, follows that in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0104786#pone-0104786-g002" target="_blank">Figure 2</a> Parts A, B and C, respectively.</p

Baseline characteristics of 10,042 contacts by participants in a diary study in the Kilifi Health and Demographic Surveillance System, Kenya.

╞<p>Missing records as a proportion of the total contacts 10,042): Relationship to participant (198, 2.0%); Sleep in same room (22, 0.8%); Ever met the contact before (298, 3.0%); Frequency of meeting (38, 0.4%).</p>$<p>While 63% of contacts with family members (parents, spouses, children and siblings),only 28% live in the same household. Members of the same family could be living in different households and share a common compound (homestead).</p>□<p>Frequency of meeting: daily (on a day-to-day basis); regularly (more than four times a week); often (once or twice a week); rarely (once or twice a month).</p