Building early warning capacity for climate-sensitive diseases in Zunyi
Categories: Action Line 1: Surveillance and monitoring, Integrated risks, monitoring, early warning, and GHG emissions tracking, Drought, Health systems wide resilience, Heat and cold, Storms, Vector distribution and ecology
Country: China
Organizations: Lancet Countdown Asia Center
The intervention
To address high rates of enteric and vector-borne infections driven by floods, heavy rainfall and summer heat, since 2019 Zunyi City has established a multisectoral, synchronized early-warning and service system that integrates meteorological services, emergency management and disease control. Through the Zunyi Meteorological-Sensitive Disease Forecasting System, the Disease Incidence Risk-Level Forecasting System and an “Intelligent Community/Home” meteorological-health service platform. Practical measures include: during meteorological anomalies the CDC provides weather-related health warnings based on meteorological forecasts and, in partnership with China Mobile, these warnings are pushed selectively to citizens’ phones via SMS; routine weather bulletins embed public-health and disaster-prevention advice supplied by CDC and emergency management; health alerts are issued at least three times per week, repeated during seasonal peaks and immediately after disasters, with rapid localized prevention and care guidance for small outbreaks; the “Intelligent Community/Home” platform delivers embedded, dynamic meteorological-health service scenarios for the elderly and other vulnerable groups; and, relying on the meteorological-sensitive disease forecasting system, “Zunyi Weather” pushes targeted health-weather alerts via WeChat, Weibo and SMS to provincial, municipal, county, township and village authorities as well as to the general public.
Success factors
The success of the intervention lay in a multi-source data–driven design and a cross-departmentally coordinated implementation. At the design stage, Zunyi fully considered the local meteorological conditions—such as heavy rainfall, flooding, and rising summer temperatures—and their close relationship with the seasonal spread of infectious diseases. By analyzing the “three elements of transmission” (sources of infection, transmission pathways, and susceptible populations), the system ensured scientific and targeted warning content. In implementation, strong cross-departmental collaboration integrated the expertise of the meteorological, emergency management, and CDC departments, while synchronized communication channels—SMS, social media, and weather forecast embedding—ensured timely and broad dissemination. In addition, the use of big data and intelligent platforms allowed automatic generation and precise targeting of risk information, especially for vulnerable groups such as the elderly. These design and implementation strategies worked together to reduce disease incidence, alleviate medical burdens, and strengthen the city’s resilience against climate-sensitive diseases.
Recommendations
For teams seeking to replicate Zunyi's approach elsewhere, priority should be given to establishing formal multi-departmental governance mechanisms (involving meteorological services, disease control/public health, emergency management, telecommunications, and local government) alongside early investment in interoperable data platforms and secure data sharing. Furthermore, forecasting models should be developed and expanded based on local meteorological factors. Analysis should proceed not only from the ‘triangle of factors’ in infectious disease transmission (source of infection — transmission routes — susceptible populations). Crucially, early warning efforts should expand beyond analyses solely linking temperature, rainfall, and infectious disease incidence to incorporate additional meteorological factors such as air pressure, wind speed, relative humidity, and maximum/minimum temperatures. Furthermore, the impact of these meteorological factors on chronic diseases should be integrated into health early warning systems to enhance the precision and operational feasibility of risk-tiered forecasting. Pilot and validate predictive models locally, establishing measurable outcome and process indicators for routine assessment to iteratively refine the system. Invest in interpretation and response capacity building at grassroots and community levels, employing targeted, repeated multi-channel information dissemination (SMS, WeChat, media, etc.) while utilising community channels to reach vulnerable populations. Ultimately, establish a group-based prevention model anchored in forecasting to tangibly reduce disease incidence and burden.
Key resources
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