PAHO, IHME Sign 5-Year Deal To Analyze Health Information, Predict The Future.

Image: PAHO. A new agreement will make it easier to analyze health information and predict future health care needs.
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By strengthening countries’ capacities to analyze data and produce accurate data, they will help decision makers shape life-saving health policies

Washington, DC, November 3, 2020 (PAHO)– The Pan American Health Organization (PAHO) and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington have partnered to improve knowledge about health conditions of the population of the Americas by strengthening data analysis capabilities and producing high-quality metrics to provide more accurate estimates in the countries of the region.

After working together between 2014 and 2019, with the recent emergence of COVID-19, PAHO and IHME signed a new 5-year memorandum of understanding.

Now, both organizations will help build professional capacity in data analysis, modeling, and forecasting methods, as well as in the joint production of high-quality metrics related to population health and its determinants, health system performance, and health emergencies.

Predictive analytics allows for the estimation of the behavior of any health challenge with an acceptable degree of uncertainty in establishing when and under what conditions countries can predict changes in disease behavior.

With this information, in cases such as the current COVID-19 pandemic, demand for acute care medical services can be estimated, time frames for partial or total lifting of mobility restrictions can be determined, the effect of preventive measures such as the generalized use of face masks can be calculated, and even new needs that might arise in subsequent waves of the pandemic can be predicted.

Predictive models have been useful in estimating the number of cases and deaths from COVID-19; the resources needed, such as hospital and intensive care unit (ICU) beds; and the demand for supplies, such as personal protective equipment (PPE).

These models become indispensable tools by providing perspectives that are crucial for policymakers, particularly in situations of high uncertainty where information based on observations is limited.

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