Bivariate spatially correlated count data appear naturally in several domains such as ecology, economy and epidemiology. Current methods for analysing such data lack simplicity, interpretability and computational awareness. This paper introduces Poisson cokriging, a bivariate geostatistical technique to model and predict spatially correlated count variables. Our method exploits classical geostatistical theory and the bivariate Poisson distribution to propose an adaptation of cokriging when the underlying process follows a bivariate Poisson structure. A simulation study and a real data application using counts from two mosquito-borne diseases in Colombia showed that our model successfully performs spatial predictions at unobserved locations under different settings. We demonstrate the competitive convenience of Poisson cokriging in filtering rates and modelling highly variant population sizes against traditional geostatistical methods. We conclude that Poisson cokriging improves prediction accuracy and reduces variance prediction errors in comparison with ordinary cokriging.

双变量空间相关计数数据自然出现在生态学、经济和流行病学等多个领域。目前分析此类数据的方法缺乏简单性、可解释性和计算意识。本文介绍了泊松协同克里金法，这是一种用于建模和预测空间相关计数变量的双变量地统计技术。我们的方法利用经典的地统计理论和二元泊松分布来提出当基础过程遵循二元泊松结构时协同克里金法的适应。使用哥伦比亚两种蚊媒疾病的计数进行的模拟研究和真实数据应用表明，我们的模型成功地在不同设置下的未观察位置进行了空间预测。我们证明了泊松协同克里金法在过滤率和对高度变异的人口规模建模方面相对于传统地统计方法的竞争便利性。我们得出的结论是，与普通协同克里金法相比，泊松协同克里金法提高了预测精度并减少了方差预测误差。