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

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.

[1]  Ning Xiao,et al.  Analysis of Malaria Epidemiological Characteristics in the People's Republic of China, 2004-2013. , 2015, The American journal of tropical medicine and hygiene.

[2]  Bernhard Schölkopf,et al.  Uncovering the structure and temporal dynamics of information propagation , 2014, Network Science.

[3]  Andrew J Tatem,et al.  Changing epidemiology and challenges of malaria in China towards elimination , 2019, Malaria Journal.

[4]  P. Fine.  The interval between successive cases of an infectious disease. , 2003, American journal of epidemiology.

[5]  Zhongjie Li,et al.  Trends of imported malaria in China 2010–2014: analysis of surveillance data , 2016, Malaria Journal.

[6]  Caroline O Buckee,et al.  Mapping malaria by combining parasite genomic and epidemiologic data , 2018, bioRxiv.

[7]  Andrew J. Tatem,et al.  Plasmodium falciparum malaria importation from Africa to China and its mortality: an analysis of driving factors , 2016, Scientific Reports.

[8]  J. Wallinga,et al.  Different Epidemic Curves for Severe Acute Respiratory Syndrome Reveal Similar Impacts of Control Measures , 2004, American journal of epidemiology.

[9]  Zhao-Liang Li,et al.  Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data , 2002 .

[10]  Wei-kang Jiang,et al.  Monitoring of malaria vectors at the China-Myanmar border while approaching malaria elimination , 2018, Parasites & Vectors.

[11]  Zhou Zj,et al.  The malaria situation in the People's Republic of China , 1981 .

[12]  W. Wernsdorfer,et al.  The clinical features of malaria. , 1988 .

[13]  G. Lu,et al.  Malaria outbreaks in China (1990–2013): a systematic review , 2014, Malaria Journal.

[14]  U. Dalrymple,et al.  The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015 , 2015, Nature.

[15]  Xiao-Nong Zhou,et al.  Historical patterns of malaria transmission in China. , 2014, Advances in parasitology.

[16]  Anthony N. Pettitt,et al.  Comment on the paper: ‘Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations’ by Rue, H. Martino, S. and Chopin, N. , 2009 .

[17]  Thibaut Jombart,et al.  epicontacts: Handling, visualisation and analysis of epidemiological contacts , 2018, F1000Research.

[18]  Li Zhang,et al.  China’s 1-3-7 surveillance and response strategy for malaria elimination: Is case reporting, investigation and foci response happening according to plan? , 2015, Infectious Diseases of Poverty.

[19]  Fang Huang,et al.  Ready for malaria elimination: zero indigenous case reported in the People’s Republic of China , 2018, Malaria Journal.

[20]  Hongjun Qiu,et al.  Risk assessment of malaria transmission at the border area of China and Myanmar , 2017, Infectious Diseases of Poverty.

[21]  Chengdong Xu,et al.  Spatiotemporal epidemic characteristics and risk factor analysis of malaria in Yunnan Province, China , 2017, BMC Public Health.

[22]  T. Jombart,et al.  epicontacts: Handling, visualisation and analysis of epidemiological contacts , 2018, F1000Research.

[23]  Andrew J Tatem,et al.  Mapping residual transmission for malaria elimination , 2015, eLife.

[24]  C. Fraser.  Estimating Individual and Household Reproduction Numbers in an Emerging Epidemic , 2007, PloS one.

[25]  Thomas A. Hennig,et al.  The Shuttle Radar Topography Mission , 2001, Digital Earth Moving.

[26]  Qi Gao,et al.  Communicating and Monitoring Surveillance and Response Activities for Malaria Elimination: China's “1-3-7” Strategy , 2014, PLoS medicine.

[27]  Bernhard Scholkopf,et al.  Submodular Inference of Diffusion Networks from Multiple Trees , 2012, ICML.

[28]  J. L. Parra,et al.  Very high resolution interpolated climate surfaces for global land areas , 2005 .

[29]  Jun Zhang,et al.  Effectiveness and impact of the cross-border healthcare model as implemented by non-governmental organizations: case study of the malaria control programs by health poverty action on the China-Myanmar border , 2016, Infectious Diseases of Poverty.

[30]  Kevin Marsh,et al.  Clinical Features of Malaria , 1999 .

[31]  Jianxing Yu,et al.  Malaria in China, 2011–2015: an observational study , 2017, Bulletin of the World Health Organization.

[32]  A. Nelson,et al.  Travel time to major cities: a global map of accessibility , 2008 .

[33]  M. Tanner,et al.  Malaria surveillance-response strategies in different transmission zones of the People's Republic of China: preparing for climate change , 2012, Malaria Journal.

[34]  Samir Bhatt,et al.  Estimating spatiotemporally varying malaria reproduction numbers in a near elimination setting , 2018, Nature Communications.

[35]  Jun Cao,et al.  Shrinking the malaria map in China: measuring the progress of the National Malaria Elimination Programme , 2016, Infectious Diseases of Poverty.

[36]  C. Dolea,et al.  World Health Organization , 1949, International Organization.

[37]  Weltgesundheitsorganisation.  World malaria report , 2005 .

[38]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[39]  Amy Wesolowski,et al.  Mapping imported malaria in Bangladesh using parasite genetic and human mobility data , 2019, eLife.

[40]  Shui-sen Zhou,et al.  Surveillance and response to drive the national malaria elimination program. , 2014, Advances in parasitology.

[41]  Amy Wesolowski,et al.  Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa , 2019, eLife.

[42]  Jun Feng,et al.  Lessons from malaria control to elimination: case study in Hainan and Yunnan provinces. , 2014, Advances in parasitology.

[43]  David L Smith,et al.  Quantitative, model-based estimates of variability in the generation and serial intervals of Plasmodium falciparum malaria , 2016, Malaria Journal.