Modelling COVID-19 Hotspot Using Bipartite Network Approach

COVID-19 causes a jarring impact on the livelihoods of people in Malaysia and globally. To prevent an outbreak community, identifying likely sources infection (hotspots) is important. The goal this study to formulate bipartite network model transmissions by incorporating patient mobility data address assumption population homogeneity made conventional models focus indirect transmission. Two types nodes – human location are main concern research scenario. 21 31 identified from patient’s pre-processed data. parameters used for node quantifications ventilation rate environmental properties that affect stability virus such as temperature relative humidity. summation rule applied quantify all link weight between node. ranking computed using web search algorithm. This considered verified error obtained comparison benchmark small. As result, higher denoted hotspot study, attached will be ranked ranking. Consequently, has risk transmission compared other locations. These findings proposed provide framework public health authorities identify high-risk groups cases control at initial stage.