Global Statistics

All countries
548,935,393
Confirmed
Updated on June 26, 2022 8:18 pm
All countries
520,730,887
Recovered
Updated on June 26, 2022 8:18 pm
All countries
6,350,765
Deaths
Updated on June 26, 2022 8:18 pm
Tuesday, February 7, 2023

Global Statistics

All countries
548,935,393
Confirmed
Updated on June 26, 2022 8:18 pm
All countries
520,730,887
Recovered
Updated on June 26, 2022 8:18 pm
All countries
6,350,765
Deaths
Updated on June 26, 2022 8:18 pm
Molderizer and Safe Shield

Borrowing from weather and climate science models can help us prepare for future pandemics

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The modeling approaches that have been incorporated into climate change and weather forecasting research, combined with their risk communication strategies, can be used to improve how the current coronavirus disease 2019 (COVID-19) pandemic is managed. A new PNAS study determines how insights from the climate and weather research field can improve the forecasting of pandemics.

Study: Learning from weather and climate science to prepare for a future pandemic. Image Credit: elRoce / Shutterstock.com

Study: Learning from weather and climate science to prepare for a future pandemic. Image Credit: elRoce / Shutterstock.com

Climate modeling to improve pandemic models

Previously established pandemic models have provided mixed results for tracking and forecasting the trajectory of the severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). Thus, improved approaches for modeling SARS-CoV-2 should involve risk communication, data exchange, international comparisons, and model development.

By adapting methods and insights from a field where modeling accuracy is crucial, such as weather and climate, the disease-modeling community can improve its ability to prepare for future outbreaks. Climate modeling, for example, comprises an international comparison of models and standardized testing, which currently does not exist for pandemic models.

In weather and climate modeling, data sharing is needed immediately. Since the 1960s, the World Meteorological Organization (WMO) has organized homogenized real-time international data exchanges that can be used as an exemplar for the World Health Organization (WHO).

How to approach uncertainties

Although current weather forecasts can provide accurate estimations of the current atmosphere state, the type and accuracy of the measurement instrument used to obtain these estimates can lead to errors.

Likewise, SARS-CoV-2 measurements are also subject to the type of instrumentation being used. For example, some diagnostic methods are susceptible to false-positive and false-negative results, whereas SARS-CoV-2 measurements obtained in sewage samples can only be used to represent the specific catchment area.

In addition to the limitations of current instruments, the oversimplification of observations can also lead to challenges in both weather and epidemic models. For example, the severity of COVID-19, the disease caused by infection with SARS-CoV-2, is often dependent upon age, sex, human behaviors, environmental conditions, and the presence of comorbidities. These factors may also alter the transmission rates of SARS-CoV-2.  

Taken together, instrumentation issues and the limitations of current modeling approaches can lead to a phenomenon known as deterministic chaos, in which minor uncertainties can cause these models to make extreme and inaccurate predictions. When weather forecasters encounter deterministic chaos, small changes are made to the initial conditions of the model to identify potential outcomes and ultimately improve the accuracy of weather forecasts.

A similar approach can be incorporated into pandemic-related communication efforts. Data assimilation, for example, can be used to develop accurate algorithms for estimating the most likely outcomes, such as the trajectory of potential disease outbreaks.

Recently, data-assimilation models have been used to collectively analyze hospitalization rates, fatalities, and SARS-CoV-2 genetic material found in wastewater samples to predict the state of the pandemic in Switzerland in the fall of 2020. This information was subsequently used to study predicted estimates of future infection, hospitalization, and mortality rates and understand how different infection containment measures might alter these projections.

Significantly, as compared to weather forecasting models, those used for COVID-19 projections cannot predict how the emergence of SARS-CoV-2 mutations might alter viral transmission and disease severity.

Global data exchange

The standardized and routine exchange of information throughout the world is essential in a global pandemic. Although preprints have remained readily available, health agencies have encountered several challenges in how to adequately document and collect data, as well as evaluate the effectiveness of interventions.

Comparatively, national meteorological agencies maintain national standardized networks, provide climate services, and collaborate with local organizations to issue warnings when needed. In an effort to overcome the limitations of current public health approaches to mitigate the pandemic, scientists have utilized the integrative network of weather systems to organize and communicate data for the public.

Risk communication

Climate change and weather forecasting approaches to uncertain situations have demonstrated that clear mandates, established procedures, clear communication, as well as trust between scientists, the public, and policymakers, are essential. The open discussion of any present uncertainties, combined with the ability to accept when mistakes have been made, are also important in supporting rational decision-making processes among the public.

Journal reference:

  • Schemm, S., Grund, D., Knutti, R., et al. (2023). Learning from weather and climate science to prepare for a future pandemic. PNAS. doi:10.1073/pnas.2209091120.



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