Elevating PCP Level Implementation: A Strategic Approach with Architecture Software in the Covid-19 Era

Between 2020 and 2021, Indonesia and Malaysia were both impacted by the COVID-19 pandemic. These two nations adopted a strikingly similar approach to restrict access to certain areas, primarily relying on identical criteria. In the case of Indonesia, the PPKM (Community Activity Restriction Enforcement) strategy was employed, this contained a component involving the exposure of COVID-19 affected persons. The determination of the extent of this exposure was primarily based on monitoring the rise in the number of individuals who had been in contact with COVID-19-positive individuals. However, during a pandemic, it's imperative to consider not only those individuals who are confirmed to have COVID-19 but also those who exhibit symptoms similar to COVID-19 but have not been diagnosed. These individuals can also act as potential agents for the spread of the pandemic within their communities. This is referred to as the "Precaution COVID-19 Pandemic (PCP) Level." It's important to note that the PCP level does not have a universally agreed-upon definition at present. Instead, it is influenced more by the number of individuals displaying symptoms akin to those of COVID-19, rather than solely relying on the increase in confirmed COVID-19 cases. In essence, both countries shared a comparable approach to pandemic management during this period, but the emergence of the PCP level underscores the need for a broader perspective when assessing the pandemic's impact on communities, considering not only confirmed cases but also symptomatic individuals who may contribute to its spread. The PCP Level determination can be used for preventative policy and to supplement the previous Pandemic Level Methods. Two methods are employed to realize this concept: the AHP approach for calculating the Covid-19 pandemic level and the K-Mean algorithm for pattern clustering. Data was gathered from 11 health centers in West Java province. The findings of this study underscore the potential of a multifaceted approach to pandemic precaution in the context of COVID-19. By synergistically integrating the three proposed algorithms and leveraging data on symptoms that are intricately linked to the predominant COVID-19 symptoms, this research illuminates a promising avenue for determining and implementing precautionary measures during the ongoing pandemic. This holistic strategy not only offers a more comprehensive understanding of the virus's spread but also furnishes decision-makers with a versatile toolkit for tailoring precautionary actions to different circumstances, ultimately contributing to more effective and adaptive pandemic management.