Chris von Csefalvay

British epidemiologist

Chris von Csefalvay (born 15 July 1986) is a Hungarian-British computational epidemiologist focusing on the computational modelling of infectious diseases. He is currently the Practice Director for biomedical AI/ML at HCLTech, where he advises NGOs, governments and the biopharmaceutical sector. The author of numerous research papers, he published his first monograph, Computational Modeling of Infectious Disease in 2023. He lives in Washington, DC with his wife, the art historian and illustrator Katie Hedrick, and their Golden Retriever.

Quotes edit

  • Somewhere — perhaps in a tropical rainforest, perhaps in the thawing permafrost soil or maybe in one of our own cities — the next pathogen to try humanity’s resilience and resourcefulness is slowly emerging.
  • Despite the advances of modern medicine, the challenges of global epidemics have only become greater. Habitat loss of viral reservoir species increases the likelihood of zoonotic spillover events. Our global trade and transportation networks enable pathogens to make their way around the world in 24 hours. Climate change is disrupting fragile ecosystems and global poverty, especially urban poverty, exacerbates the problem.
  • It often takes years to create a viable antibody test as accurate as PCR-based testing. But in less than six weeks, biotech companies—approached by the U.S. government through the White House-created public-private partnership—have already seen their efforts bear fruit. This is a tribute to the incredible creative potential of the biotech sector, but it also shows the power of free enterprise, unshackled by government bureaucracy. It took more than America’s best scientists to rise to the occasion: it took a regulatory regime to let them do so.
    • A Remarkable Leap Forward, City Journal, April 21 2020
  • Today, the efforts waged to curb the COVID-19 pandemic may be the first example of a large-scale, global data-driven response to a worldwide crisis, and as such perhaps the first war of data science.

Computational Modeling of Infectious Disease (2023) edit

  • The small town of Gunnison, Colorado, lies at the bottom of the valley carved by the Gunnison River into the Rocky Mountains. It is now crossed by the Colorado stretch of U.S. Highway 50, but in 1918 the town was mainly supplied by train and two at best mediocre roads. When the 1918–19 influenza pandemic reached Colorado as an unwelcome stowaway on a train carrying servicemen from Montana to Boulder, the town of Gunnison took decisive action. As the November 1, 1918, edition of the Gunnison News-Champion documents, a Dr. Rockefeller from the nearby town of Crested Butte was “given entire charge of both towns and county to enforce a quarantine against all the world”. He instituted a strict reverse quarantine regime that almost entirely isolated Gunnison from the rest of the world. Gunnison became one of the few communities that largely escaped the ravages of the influenza pandemic, at least in the beginning. In an instructive example of the limited human patience for the social, psychological, and economic disruption of quarantine, adherence eventually waned, and the front page of the Gunnison News-Champion’s March 14, 1919, issue reports that the influenza pandemic got to Gunnison, too Nevertheless, Gunnison had a very lucky escape, of a population of over 6900 (including the county), there were only a few cases and a single death.
  • We may think of maps and mapping as an objective process, but that would be an illusion. What gets mapped, and more importantly, what does not, is a product of various social, economic, and political phenomena. Quite apart from border disputes and contentious sovereignty, mapping also reflects political priorities. Creating the survey data that can be used in maps is expensive, and large-scale mapping endeavors are typically the preserve of states, whose ability to deliver that data often depends on resources that compete with other governmental priorities. This is true especially in resource-constrained settings.
  • The term 'natural immunity' has been often used to express post-infectious immunity and differentiate it from vaccine-induced immunity. In practice, this is not necessarily helpful. There is nothing fundamentally "unnatural" in vaccine-induced immunity, and while the minutiae of natural infection and vaccine-induced immunity might differ, this is a quintessentially unhelpful notion.
  • At the time of writing, the COVID-19 pandemic has been raging for almost three years. It has cost five million people their lives. The toll of destruction, the human cost, and the economic losses remain to be counted. Few outbreaks in history leave this kind of lasting mark on society: the Plague of Athens (430 BC), the Plague of Galen (165–180 AD), the Plague of Justinian (541–549), the Black Death (1346–1353), the Spanish Flu (1918–1920), and the HIV/AIDS pandemic (1981 onwards) are the most notable exceptions. COVID-19 has now joined the ranks of these sad episodes of human history. Yet humans are not helpless against pandemics. Amidst all the destruction and grief of the COVID-19 pandemic, science has been a bright, shining beacon showing how humanity can prevail against fearful odds.
  • Computational models of infectious disease can make all the difference in our response to pandemics. As habitat loss and climate change make zoonotic spillover events increasingly more likely, COVID-19 is almost certainly not the last major pan- demic of the 21st century. In fact, it is reasonable to assume that such outbreaks will become increasingly frequent. Computational models can be powerful weapons in our fight against pandemics.
  • Hesiod’s description of Pandora’s box reminds us that though we live in a world of danger, where infectious diseases continue to maim and kill millions, especially across the developing world, we are not without hope. Part of that hope is our ability as humans to bring mathematics, genomics, data science, statistics, and computational science to bear on this problem and call these altogether rather disparate disciplines into humanity’s service against disease. Infectious disease modeling is part of that wider story of hope.
  • The same dynamics that keep us safe in a pack, herd or society, and comfortable in our family, friends or neighbours also serves as a way for pathogenic transmissions. The warmth of a human dwelling or the immense complexity of a bee hive is also an opportunity for a pathogen to tap into a susceptible population. Network interdiction is a comprehensive name for algorithms intended to disrupt such connections.

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