# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 1 Introduction  
Infectious diseases are diseases caused by microorganisms [1]. Many infectious diseases are highly contagious, such as COVID-19, monkeypox, chickenpox, influenza, and so on, and these have seriously affected or are currently seriously affecting human health, economic activities, education, sports, and leisure. Restriction, tracing, and isolation of people's movements when an epidemic spreads are effective ways to slow its spread [2, 3]. A GNSS allows a tiny electronic receiver to determine its position, including its longitude, latitude, and altitude (elevation), with an accuracy of a few centimeters to a few meters by using time signals transmitted by a satellite radio along the line of sight [4, 5]. Since GNSSs can provide the precise location information of targets, they have been widely used in various remote sensing applications [6, 7, 8, 9, 10, 11, 12], such as in epidemic monitoring and geographic information systems. In this study, we design a method of using longitude and latitude coordinates to calculate if a large number of targets are in an area while considering a large number of targets and a wide range of the target area. Under the demand of epidemic prevention, we want to know in real-time whether the targets are in a specific range or stay within the official designation. Therefore, we only need to define this range, and our method can obtain the answer. In geography, an area can be represented by a polygon. A target is a geographic point, which is a point with latitude and longitude coordinates. Determining if a geographic pointis within an area is a PtInPy problem. Much of the literature on such issues is focused on special conditions relating to points and polygons [13; 14; 15; 16; 17; 18]. In simple terms, every method contains a PtInPy function and uses a geographical point and a polygon as its input. This PtInPy function can confirm if the point is an inner point of this polygon or an outer point of this polygon. When the number of targets is huge, it is inefficient to confirm them one by one by using a PtInPy function. However, there may be many targets to be tracked in many areas, such as in epidemic management. Take the coronavirus disease 2019 as an example. It is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The first known case was in Wuhan, China, in December 2019. After that, the disease spread worldwide, leading to the COVID-19 pandemic. According to the World Health Organization's weekly report, as of 22 May 2022, over 522 million confirmed cases and over 6 million deaths had been reported globally [19]. This example generated many targets that were distributed over broad and numerous areas. The CDC often determines its management strategy by tracking targets within such areas. To find the targets in a specific area, they can be confirmed one by one by using a PtInPy function. However, this way could be more efficient because the number of targets is large, and the targets within an area represent only a tiny fraction of the total targets. The number of times that a PtInPy function is used reflects the performance of a method in finding targets within a given area. In [20; 21], methods for reducing the use of PtInPy functions were proposed. In [20], the authors covered an area with a rectangle, and a PtInPy function was used only for the targets within this rectangle. However, because the range of the rectangle could be much more extensive than the range of the target area, the use of the PtInPy function was still very high. In [21], the authors considered that the number of targets was huge. Therefore, they also included a way to reduce the number of points that were confirmed by the PtInPy function. However, this method has two main areas for improvement. First, the range of a small area is significant, but there needs to be a plan in the technique. Second, the small areas planned by this method overlapped. These shortcomings caused a bottleneck in the performance of this method.