Details

Autor(en) / Beteiligte

• Saxena, Aseem
• Kumar, Manish
• Mukherjee, Santanu
• Tyagi, Vinay Kumar
• Ngiehm, Long D
• Kuroda, Keisuke
• Vithanage, Meththika
• Ngiehm, Long D.

Titel

Prediction in the Context of Viral Pandemics: A Special Emphasis on SARS-CoV-2

Ist Teil von

• Wastewater Surveillance for Covid-19 Management, 2024, Vol.125, p.289-304

Ort / Verlag

Switzerland: Springer International Publishing AG

Erscheinungsjahr

2024

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Pandemics are the widespread occurrence of a new infectious disease affecting people across a large region, often globally. The spread of a pandemic typically involves several pandemic dynamics like emergence of a novel pathogen, global spread, human behaviour, health care infrastructure, and public health measures. During the pandemic, the high transmission rates of viruses and bacteria led to a critical situation, resulting in significant social and economic disruptions as well as substantial impact on health systems. Accurate prediction of COVID-19 could facilitate clinical decision-making and medical resource allocation, this can be possible through predictive modelling by using clinical and wastewater surveillance (WBS) data. Therefore, in this review article we highlighted the importance of predictive modelling and associated factors in order to offer precise and reliable predictions of the evolution of the pandemic and analyse the possible impact of actions for supporting policy makers in terms of decision-making in the area of public health. On the basis of pandemic dynamics, different modelling approaches were used in this article on the basis of their features, objectives, and key findings to understand the severity of COVID-19.