Climate change is intensifying infectious and chronic diseases like malaria and diabetes, respectively, especially among the vulnerable populations. Global temperatures have risen by approximately $0.6^\circ$C since 1950, extending the window of transmission for mosquito-borne infections and worsening outcomes in diabetes due to metabolic stress caused by heat. People living with diabetes have already weakened immune defenses and, therefore, are at an alarmingly increased risk of contraction of malaria. However, most models rarely include both ways of interaction in changing climate conditions. In the paper, we introduce a new compartmental epidemiological model based on synthetic data fitted to disease patterns of India from 2019 to 2021. The framework captures temperature-dependent transmission parameters, seasonal variability, and different disease dynamics between diabetic and non-diabetic groups within the three-compartment system. Model calibration using Multi-Start optimization combined with Sequential Quadratic Programming allows us to find outstanding differences between populations. The odds of malaria infection in diabetic individuals were found to be 1.8--4.0 times higher, with peak infection levels in 35--36\%, as compared to 20--21\% in the non-diabetic ones. The fitted model was able to capture well the epidemiological patterns observed, while the basic reproduction number averaged around 2.3, ranging from 0.31 to 2.75 in different seasons. Given that India's diabetic population is set to rise to about 157 million people by 2050, these findings point to a pressing need for concerted efforts toward climate-informed health strategies and monitoring systems that address both malaria and diabetes jointly.

翻译：气候变化正分别加剧疟疾等传染病和糖尿病等慢性病的流行，特别是在脆弱人群中尤为显著。自1950年以来，全球气温已上升约$0.6^\circ$C，这既延长了蚊媒传染病的传播窗口期，又因热应激引发的代谢压力而恶化了糖尿病患者的健康状况。糖尿病患者本身免疫防御能力较弱，因此在气候变化条件下感染疟疾的风险显著增加。然而，现有模型很少同时纳入这两种疾病在气候变迁中的双向交互作用。本文基于2019年至2021年印度疾病模式的合成数据，构建了一个新的仓室流行病学模型。该框架在三仓室系统中整合了温度依赖的传播参数、季节性变异以及糖尿病与非糖尿病人群间的疾病动力学差异。通过结合多起点优化与序列二次规划进行模型校准，我们发现了人群间的显著差异：糖尿病患者感染疟疾的几率是非糖尿病患者的1.8-4.0倍，其感染峰值达35-36%，而非糖尿病人群仅为20-21%。校准后的模型能较好地拟合观测到的流行病学模式，其基本再生数平均值约为2.3，不同季节在0.31至2.75之间波动。鉴于印度糖尿病患者预计到2050年将增至约1.57亿人，本研究结果凸显了亟需制定气候智能型健康战略并建立监测系统，以协同应对疟疾与糖尿病的双重挑战。