Tyler O'Neal, Staff Editor APPLICATIONS

Colorado COVID-19 modeling team shows how its models helped reduce spread by informing state policies amidst the pandemic

Colorado researchers have published new findings in Emerging Infectious Diseases that take a first look at the use of SARS-CoV-2 mathematical modeling to inform early statewide policies enacted to reduce the spread of the Coronavirus pandemic in Colorado. Among other findings, the authors estimate that 97 percent of potential hospitalizations across the state in the early months of the pandemic were avoided as a result of social distancing and other transmission-reducing activities such as mask-wearing and social isolation of symptomatic individuals.

The modeling team was led by faculty and researchers in the Colorado School of Public Health and involved experts from the University of Colorado Anschutz Medical Campus, University of Colorado Denver, University of Colorado Boulder, and Colorado State University.

"One of the defining characteristics of the COVID-19 pandemic was the need for rapid response in the face of imperfect and incomplete information," said the authors. "Mathematical models of infectious disease transmission can be used in real-time to estimate parameters, such as the effective reproductive number (Re) and the efficacy of current and future intervention measures, and to provide time-sensitive data to policymakers."

The new paper describes the development of such a model, in close collaboration with the Colorado Department of Health and Environment and the Colorado Governor's office to gauge the impact of early policies to decrease social contacts and, later, the impact of gradual relaxation of Stay-at-Home orders. The authors note that preparing for hospital intensive care unit (ICU) loads or capacity limits was a critical decision-making issue.

The Colorado COVID-19 Modeling team developed a susceptible-exposed-infected-recovered (SEIR) model calibrated to Colorado COVID-19 case and hospitalization data to estimate changes in the contact rate and the Re after emergence of SARS-CoV-2 and the implementation of statewide COVID-19 control policies in Colorado. The modeling team supplemented model estimates with an analysis of mobility by using mobile device location data. Estimates were generated in near real-time, at multiple time points, with a rapidly evolving understanding of SARS-CoV-2. At each time point, the authors generated projections of the possible course of the outbreak under an array of intervention scenarios. Findings were regularly provided to key Colorado decision-makers.

"Real-time estimation of contact reduction enabled us to respond to urgent requests to actively inform rapidly changing public health policy amidst a pandemic. In early stages, the urgent need was to flatten the curve," note the authors. "Once infections began to decrease, there was interest in the degree of increased social contact that could be tolerated as the economy reopened without leading to overwhelmed hospitals."

"Although our analysis is specific to Colorado, our experience highlights the need for locally calibrated transmission models to inform public health preparedness and policymaking, along with ongoing analyses of the impact of policies to slow the spread of SARS-CoV-2," said Andrea Buchwald, Ph.D., lead author from the Colorado School of Public Health at CU Anschutz. "We present this material, not as a final estimate of the impact of social distancing policies, but to illustrate how models can be constructed and adapted in real-time to inform critical policy questions."

SMU prof wins NSF grant for supercomputer models to better aid evacuees after natural disasters

Tyler O'Neal, Staff Editor APPLICATIONS

We can do better with a systems approach instead of catching up after each hurricane

Halit Uster, an engineering professor at SMU, has been awarded a three-year National Science Foundation (NSF) grant of $315,580 to investigate integrated evacuation planning and disaster preparedness models that offer relief to evacuees in a more robust, predictive, timely, and cost-effective manner than was seen in past natural disasters. Halit Üster, an engineering professor at SMU, has been awarded a three-year NSF grant for models to better aid evacuees after natural disasters like Hurricanes Harvey, Katrina.

The optimization and simulation models that will be developed with a systems view will load in information such as where the disaster is expected to occur and its intensity, how many people are expected to flee those places, where they are likely to travel, and how long on average it will take them to get from one spot to another. Using that data, the model will determine the most cost-effective options to move people out of harm's way, make sure those people have enough supplies where they're going, and do all of this as quickly as possible.

This approach will help decision-makers put shelters and the larger supply locations that support those shelters in the right places and size, Uster said, before damaged infrastructure and transportation routes prevent options.

"The complexity of such coordination became obvious in the recent events of Hurricanes Harvey and Irma in 2017 as well as earlier ones, such as Katrina and Rita in 2005. These events created significant awareness of shortcomings in existing response plans," said Uster, who is a professor of Operations Research and Engineering Management at SMU's Lyle School of Engineering.

Too often, Uster said, emergency response plans don't recognize the interdependence that evacuation activities play with the supply-side, and vice versa.

Decision-makers in charge of getting people out of danger from a hurricane or flood are most focused on the removal of people from the danger zone, but they don't give as much thought to where those evacuees are heading, Uster said.

"On the other hand, you have people on the supply side, who are thinking, 'We have people who are moving to different locations. We have to get supplies for them.' But they don't know where those people are going to go," he added.

Planners are left with the choice of sending fewer supplies to various places where they think evacuees might go, or waiting to see what happens with a storm and then trying to send supplies where they are most needed. That approach has not been successful, Uster said.

"If you wait, the hurricane can come in, and the conditions change. So you can't send supplies in time to those places, and it quickly becomes a mess. We can do better proactive planning using a systems approach instead of trying to catch up when things go wrong."

Uster built an earlier model after Hurricanes Katrina and Rita in 2005. He was living in College Station at the time and saw first-hand how that disrupted life in Houston and its environs when so many evacuees fled there.

"When Rita happened, people had learned about the need to evacuate from their experience with Katrina just weeks before, and overnight, we had people everywhere. You could not find anything in any grocery store, and people were just spending nights at gas stations because they couldn't find a place to sleep," he said.

The NSF grant will enable Uster to add more variables and detail to his earlier model and develop solution methods to solve the complicated mathematical optimization models using real data managed by geographical information systems (GIS) using SMU's own supercomputing facilities. For example, he and SMU students will use more recently available data science tools to look through post-event surveys that report why people in harm's way evacuated or didn't, and where they evacuated to look for trends that will build a stronger preparedness model.

"There will always be some uncertainty with a natural disaster," he said. "But our goal is to minimize that and help guide evacuees to open shelters as well as provide them with supplies in a cost-effective, timely fashion."

Germany's GERICS data shows new details on climate change; predicts possible heavy rain, heat on a county level 

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The GERICS climate outlooks show climate changes at this regional scale for the first time. Each of the 401 climate outlooks is pooled at the county, district, regional district, or city level; and summarizes the results for 17 climate parameters such as temperature, heat days, dry days, wind speed, or heavy rain days on several pages. The results show projected development trends in climate parameters over the course of the 21st century: for a scenario with sufficient climate protection, a scenario with moderate climate protection, and a scenario without effective climate protection. The advantage is that the reports are uniformly structured and thus allow clear comparison. Heat – Effects of Global Warming can also be seen in German counties. Image: Andrey Grinkevich via Unsplash

"The data shows how the climate may change in the individual German regions. This provides not only citizens but also decision-makers in business and politics with a factual basis for long-term decisions. For example, for urban energy suppliers or for the adaptation of infrastructures," says Dr. Diana Rechid who is co-author of the reports together with Dr. Susanne Pfeifer and Dr. Sebastian Bathiany.

The data allow direct comparison

The analysis of the data took one year. The results show where climate change could be most severe in Germany. For each of the 401 areas examined, a climate outlook has been created individually. For example, the climate outlook for the county of Nordfriesland shows that if emissions remain high, various climate and weather phenomena may increase by the end of the century. This applies to sultry temperatures, tropical nights, prolonged periods of a heatwave, and also heavy rain. In the mountainous regions of the Alps or the Black Forest, particularly strong warming is to be expected under such conditions.

"According to our research, there is not a single county in which everything would remain the same if emissions continued at the same level or even increased. The question is: What can we avoid through effective climate protection; and what changes do we need to prepare for in any case?" asks author Diana Rechid. Thus, the climate outlooks are not only a helpful source of information for experts, politicians, and authorities. All citizens can compare the results of their hometown with those of other counties - be it due to a planned change of residence, a decision to acquire property, or to protect themselves against climate change in general.

An elaborate methodology

The data analysis methods for the current reports are based on a new evaluation software called CLIMDEX that was specifically developed for this purpose at GERICS. In addition, statistical methods are used to calculate the "robustness" of the model results to assess the resilience of the projected climate changes. Since the analyses are standardized and fully automated, they will provide a good basis for quality-approved evaluations in the future. The climate outlooks are based on observational data from the HYRAS dataset of the Deutscher Wetterdienst (DWD) and future projections of regional climate models.

A total of 85 simulations with a resolution of 12.5 kilometers were created by many European research institutions by refining the results of global climate models with different regional climate models. "They allow an assessment of different future scenarios in line with the latest scientific findings," says author Sebastian Bathiany. "Even with a lot of climate protection, we have to adapt to changes. This is precisely why climate projections are so important for the future. This provides a more accurate basis for adaptation to climate change at the local level."

The Helmholtz-Zentrum Hereon is part of the Helmholtz-Klima Initiative, where researchers perform research on climate change at a systemic level. A total of 15 Helmholtz Centers combine their climate expertise in 13 research projects. GERICS directs the Cluster Netto-Null - Pfade zur Klimaneutralität 2050.