Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach

Abstract

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.

Fig 1

Boxplots showing R_c estimates for P. vivax (A and B) and P. falciparum (C and D), aggregated by year (A and C) and month (B and D) of symptom onset. Points represent individual R_c estimates. Boxplots show median, upper and lower quartiles for R_c each.

Fig 2

Map of R_c estimates by year for A) P. vivax and B) P. falciparum. Blue points represent locally acquired cases; red points represent imported cases. The diameter of the point represents the size of the R_c estimate. Base map of administrative boundaries come from Open Street Map and its contributors.

Fig 3

Map of risk of R_c > 0 and uncertainty in this estimate (standard deviation) from application of a Gaussian Process geostatistical model with a logit link function to times and locations of observed cases for A) P. vivax and B) P. falciparum malaria across Yunnan province in each year 2011–2016. This represents the risk of a case having an R_c > 0 if observed, stratified by year.

Fig 4

A) Black points show estimated individual R_c values, blue line represents prophet model predictions for mean R_c on that day, shaded blue area shows 95% credible interval of prediction. B) Decomposed time series model, showing the general trend, fitted holiday effect and seasonal effect. For seasonal and holiday effects the y axis shows the percentage increase or decrease in R_c predicted which is attributable to a seasonal or holiday effect.

Fig 5. Red lines show 300 draws from the prior distribution used in the analysis for the Serial Interval distribution.

The black solid line represents the expected function and the dashed lines represents the 2.5 (dashed) and 97.5 (dot-dashed) quantile values of the prior distribution for the shaping parameter, α.