Malaria elimination on Hainan Island despite climate change

Abstract

Background: Rigorous assessment of the effect of malaria control strategies on local malaria dynamics is a complex but vital step in informing future strategies to eliminate malaria. However, the interactions between climate forcing, mass drug administration, mosquito control and their effects on the incidence of malaria remain unclear.

Methods: Here, we analyze the effects of interventions on the transmission dynamics of malaria (Plasmodium vivax and Plasmodium falciparum) on Hainan Island, China, controlling for environmental factors. Mathematical models were fitted to epidemiological data, including confirmed cases and population-wide blood examinations, collected between 1995 and 2010, a period when malaria control interventions were rolled out with positive outcomes.

Results: Prior to the massive scale-up of interventions, malaria incidence shows both interannual variability and seasonality, as well as a strong correlation with climatic patterns linked to the El Nino Southern Oscillation. Based on our mechanistic model, we find that the reduction in malaria is likely due to the large scale rollout of insecticide-treated bed nets, which reduce the infections of P. vivax and P. falciparum malaria by 93.4% and 35.5%, respectively. Mass drug administration has a greater contribution in the control of P. falciparum (54.9%) than P. vivax (5.3%). In a comparison of interventions, indoor residual spraying makes a relatively minor contribution to malaria control (1.3%-9.6%).

Conclusions: Although malaria transmission on Hainan Island has been exacerbated by El Nino Southern Oscillation, control methods have eliminated both P. falciparum and P. vivax malaria from this part of China.

Keywords: Diseases; Infectious diseases.

Fig. 1. Summary of the Qiongzhong highland malaria epidemic between 1995 and the elimination of endemic malaria in 2010.

a Monthly incidence of Plasmodium vivax (orange) and Plasmodium falciparum (green) and the Niño 3.4 index (black line). The Niño 3.4 index uses a 5-month running mean. b The changes in malaria prevalence in the population measured using an annual blood examination. c Malaria control strategy used in the study area. Antimalarial drug campaigns (orange), area of indoor spraying with the pesticide dicophane (DTT) (purple), and use of deltamethrin-treated nets (green). IRS, indoor residual spraying. ITNs, insecticide-treated bed nets. MDA, mass antimalarial drug administration.

Fig. 2. Mathematical model simulations of clinical malaria cases.

a The time series of number of clinical cases. b The seasonal pattern of number of clinical cases. Observations are shown as points (number of reported malaria cases). The solid lines correspond to the simulations, and the dark grey indicates the 95% credible intervals. We only present the final results, focusing on the median of posterior distributions and 95% credible intervals extracted from 10,000 runs. Seasonal pattern for the observed cases (points and error bars represent average monthly cases and the 95% confidence interval, respectively) and the median of model simulations (black line).

Fig. 3. Malaria prevalence in humans.

a P. vivax and b P. falciparum. The annual malaria prevalence was obtained from mass blood examinations. The observed data are plotted as green (P. vivax) and orange (P. falciparum) points with 95% confidence interval. The solid lines correspond to the malaria prevalence in humans estimated by epidemic model, and the grey areas correspond to pointwise 95% credible intervals.

Fig. 4. Changes in endemicity and effects of interventions conducted from 1995 to 2010.

a P. vivax and b P. falciparum. Bars represent the predicted cumulative number of clinical malaria cases averted by interventions at the end of each year. The specific contribution of each intervention is distinguished after accounting for the effect of long-term climate forcing using mechanistic models. Mathematical models were fit to epidemiological data collected from 1995 to 2010, as well as ITNs, IRS, and MDA interventions. See the Methods for a detailed description of the mathematical models. c The time (years) required to control the annual malaria prevalence <0.1% for P. vivax and d P. falciparum. Malaria prevalence as a function of ITNs and MDA interventions in the simulation beginning in 1995. Estimates were obtained with all other covariates set to original values, such as climate conditions and population.