"The goal of forecasting is not to predict the future but to tell you what you need to know to take meaningful action in the present." -- Paul Saffo, Futurist
Minimizing cognitive load: Disease forecasting models can help minimize the cognitive load of trying to assess the trajectory of an outbreak. The healthcare decision makers can instead focus on getting the basics of their outbreak prevention strategies right. For example, during the ongoing COVID19 pandemic, the COVID19 case load forecasting tools can help reduce the cognitive load for healthcare decision makers in trying to understanding the future impact of the pandemic. Therefore these data driven tools allow them to focus on their efforts to improve the compliance for basic preventative measures such as:
Routine hand washing
Wearing of face masks in public places
Quarantine the exposed and isolate the infected
Better communication strategies for the public: Forecasting tools can be really helpful to minimize the uncertainty around the future impact of a disease outbreak. This can be in-turn used to create better communication strategies. For example, through the integration of disease forecasting with public health communication strategies, instead of announcing outbreak prevention measures on-the-fly, a softer and more compassionate approach of step-wise intensification of the disease control measures can be implemented. This could result in better public compliance and help improve the public trust in health decision makers.
Minimizing health costs: By better preparing for the future impact of a disease outbreak, the healthcare systems can minimize the unpredictable shocks in costs associated with providing care to their patients. For example, by using forecasting tools that predict the weekly case loads of COVID19 patients, hospitals can improve their stocking of oxygen cylinders. The forecasting driven stocking approach can minimize the impact of sudden shocks in the availability of medical oxygen due to the disruption of the established supply-chain channels. These data driven stock management systems can also protect hospitals from the unforeseen price increases while sourcing essential medical items such as oxygen from new sources.
Assessing effectiveness of disease control measures: By comparing the predictions and the ground truth of the case loads for a disease outbreak, a data driven assessment of the disease control measures can be performed.
Effective resource management: Forecasting of disease outbreaks can help healthcare decision makers with making more accurate resource management decisions. For example, by forecasting the state-wise case load of the COVID19 patients, a particular state can easily evaluate the adequacy of the number of hospital beds available for COVID19 care.
Implementing better prevention strategies: Predicting the pattern of the disease trajectory for a given geography can result in the implementation of more effective prevention strategies. Instead of implementing a single disease prevention policy across the country, each state could be given the freedom to implement their own more effective and efficient disease control strategies. This tailored approach can minimize the associated social and economic costs of controlling a disease outbreak. For example, during the current COVID19 pandemic, a forecasting tool can be helpful in optimizing the number of tests each states should be performing to deliver a reasonably accurate picture of the pandemic in their respective communities. These data driven testing strategies that are utilizing both the past and the future impact of the disease, can help minimize the costs associated with rolling out the tests.
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