Poisson and Negative Binomial Regression Models are often used to describe the relationship between a count dependent variable and a set of independent variables. However, these models fail to analyze data with an excess of zeros, being Zero-Inflated Poisson (ZIP) and Zero-Inflated Negative Binomial (ZINB) models the most appropriate to fit this kind of data. To Incorporate the spatial dimension into the count data models, Geographically Weighted Poisson Regression (GWPR), Geographically Weighted Negative Binomial Regression (GWNBR) and Geographically Weighted Zero-Inflated Poisson Regression (GWZIPR) have been developed, but the zero-inflation part of the negative binomial distribution is undeveloped in order to incorporate the overdispersion and the excess of zeros, as was at the beginning of the COVID-19 pandemic, whereas some places were having an outbreak of cases and in others places, there were no cases yet. Therefore, we propose a Geographically Weighted Zero-Inflated Negative Binomial Regression (GWZINBR) model which can be considered a general case for count data, since locally it can become a GWZIPR, GWNBR or a GWPR model. We applied this model to simulated data and to the cases of COVID-19 in South Korea at the beginning of the pandemic in 2020 and the results showed a better understanding of the phenomenon compared to the GWNBR model.

泊松和负二项回归模型通常用于描述计数因变量和一组自变量之间的关系。然而，这些模型无法分析含有过多零的数据，零膨胀泊松 (ZIP) 和零膨胀负二项式 (ZINB) 模型最适合拟合此类数据。为了将空间维度纳入计数数据模型，已经开发了地理加权泊松回归（GWPR）、地理加权负二项回归（GWNBR）和地理加权零通胀泊松回归（GWZIPR），但其中的零通胀部分负二项分布尚未开发出来，以便将过度分散和过多的零纳入考虑范围，就像在 COVID-19 大流行开始时一样，有些地方正在爆发病例，而另一些地方还没有病例。因此，我们提出了地理加权零膨胀负二项回归（GWZINBR）模型，该模型可以被视为计数数据的一般情况，因为在本地它可以成为 GWZIPR、GWNBR 或 GWPR 模型。我们将该模型应用于模拟数据以及 2020 年大流行初期韩国的 COVID-19 病例，结果显示，与 GWNBR 模型相比，我们更好地理解了这一现象。