Typhoid fever in Santiago, Chile: Insights from a mathematical model utilizing venerable archived data from a successful disease control program

Abstract

Typhoid fever is endemic in many developing countries. In the early 20th century, newly industrializing countries including the United States successfully controlled typhoid as water treatment (chlorination/sand filtration) and improved sanitation became widespread. Enigmatically, typhoid remained endemic through the 1980s in Santiago, Chile, despite potable municipal water and widespread household sanitation. Data were collected across multiple stages of endemicity and control in Santiago, offering a unique resource for gaining insight into drivers of transmission in modern settings. We developed an individual-based mathematical model of typhoid transmission, with model components including distinctions between long-cycle and short-cycle transmission routes. Data used to fit the model included the prevalence of chronic carriers, seasonality, longitudinal incidence, and age-specific distributions of typhoid infection and disease. Our model captured the dynamics seen in Santiago across endemicity, vaccination, and environmental control. Both vaccination and diminished exposure to seasonal amplified long-cycle transmission contributed to the observed declines in typhoid incidence, with the vaccine estimated to elicit herd effects. Vaccines are important tools for controlling endemic typhoid, with even limited coverage eliciting herd effects in this setting. Removing the vehicles responsible for amplified long-cycle transmission and assessing the role of chronic carriers in endemic settings are additional key elements in designing programs to achieve accelerated control of endemic typhoid.

Fig 1. Typhoid model framework.

Transmission route diagram for typhoid model with literature-derived and fitted parameters, described in Tables 1 and 2. Disease state-specific contributions to short- and long-cycle composite of contaminated vehicles of transmission (CCVT) are represented, along with seasonal attenuation and age-susceptibility mechanisms.

Fig 2. Age distributions of typhoid in Santiago and the model.

A. Age-specific incidence in Area Norte, Santiago (by year of age) B. Age-specific exposure mechanism with equation 1-(20- A) / (A*S + 20), where A is age in years, and S is the slope of the curve.

Fig 3. Typhoid fever and economic indicators over time in Santiago.

Monthly and yearly incidence of typhoid, plotted with economic indicators including % change in GDP compared to 1960 as a baseline, price of copper in United States dollars per pound, inflation indicated by the annual change in the consumer price index, and unemployment percentage of the estimated labor force [45]. Timing of the interventions and mEL parameters are depicted.

Fig 4. Fit of model to Santiago dynamics of typhoid fever.

Fit of model (in blue) to data (•) informing the prevalence of chronic carriers(A), typhoid seasonality, divided by cases resulting from exposure to the short- vs. long cycle (B), and the distribution of age-specific typhoid incidence during and after the vaccine period (C).

Fig 5. Time trends in the model and data.

A. The model’s best fit estimates for vaccine and environmental impacts during the study period. B. Percentage of 25 year-olds that have ever been exposed to S. Typhi over the study period, as predicted by the model. C. Longitudinal model estimates with Santiago data (•) for a range of ES and Rc values. Background color indicates scaled likelihood values for the model’s fit to pre-1976 and post-1992 Santiago data, with red indicating better fits.