Seasonal Population Movements and the Surveillance and Control of Infectious Diseases

Abstract

National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.

Figure 1. Seasonal movement and infectious disease dynamics

A) Schematic incidence of an infection (blue line) showing cases (y axis) against time of year (x axis). A seasonal peak of incidence follows the summer. The underlying driver of this seasonal cycle is changes in the net reproductive number R_E, or the number of new infections per infectious individual, because R_E is the product of the basic reproductive number (R₀, or the number of new infections in a completely susceptible population), and S, the size of the accessible susceptible population, which varies seasonally following human behavior, but also susceptible depletion by infection. Two possible scenarios are illustrated: B) Aggregation of children in schools after the summer holiday (red arrows) increases effective contact among susceptible individuals, thus altering R_E and increasing incidence (reported for many childhood infections [1, 50, 54]); or C) Aggregation in cities after the period of agricultural work where communities have migrated out to rural settings (red arrows) results in an increase in R_E (reported for measles in Niger [5])

Figure 2. Seasonal reporting biases in disease incidence

In resource poor settings, the numerator of incidence generally under-estimates cases that have occurred as a result of under-reporting and information loss at a range of scales; here, depicted by a triangle where the narrow point indicates the final data that filters through the surveillance system to the ministry of health (MoH). The filter might also fluctuate seasonally: access to medical health centers where reporting occurs is likely to fluctuate seasonally, for reasons ranging from roads being washed out, to no one being available to accompany sick individuals during busy times such as harvest season. Misdiagnosis might also vary seasonally, for example, where at particular times of year, the expectation is that malaria is the core cause of fever. Finally, the denominator might also vary seasonally as a result of large-scale movements of population, for example linked to agriculture.

Figure 3. New directions for characterizing human seasonal movement, and evaluating its effects on infectious disease dynamics

A) A hypothetical population consisting of three patches, a focal site (red), where density varies from low (pale red) to high density (dark red) over the course of the year (x axis) following movement to two smaller patches (red arrows) which are unevenly visited (arrow size); associated pathogen incidence (y axis) over the same time-course (x axis) is shown below (blue line); BB) Potential sources of inference into this system include integrated Bayesian models of population density (top panel) that incorporate seasonally fluctuating sources (extending on [49]); directional information on population flows, such as those available from call data records (CDRs) (middle panel [23, 50, 55]); and methods which combine information on human density and movement with that on infectious disease incidence (bottom panel) using mathematical models to strengthen inference into core parameters such as R₀ [33].