Computational Modeling of Infectious Disease Quotes

“The history of epidemics in human populations has always been closely connected to cities. From the Great Plague of Athens (430 BC) to the COVID-19 pandemic, cities have played a unique role in the lives of epidemics that affect human populations. The increased population density provides the pathogen with a vastly increased likelihood that during a given infectious period, an infected individual will make contact with a susceptible individual. Overcrowding and urban poverty also directly affect other epidemic processes, e.g. attracting vectors who thrive on the byproducts of urban human existence.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“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 pandemic of the 21st century.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“One of the most complex computer games ever devised is called Dwarf Fortress. It is not much to look at: its graphics are the terminal-based structures that were in vogue in the 1980s. What makes Dwarf Fortress an extraordinary game is the depth of agent-based logic: every character, every enemy unit, even pets are endowed with a hugely complex agent-based behavioural model. As an example, cats in Dwarf Fortress can stray into puddles of spilled beer, lick their paws later, and succumb to alcohol poisoning.

Yet agent-based modeling is about much more than belligerent dwarves and drunk cats. Agent-based models are powerful computational tools to simulate large populations of boundedly rational actors who act according to preset preferences, although often enough in a stochastic manner.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“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.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“The world came uncomfortably close to a pandemic in late 1989, when – largely overshadowed by the sweeping political changes and the end of the Cold War –, cynomolgus monkeys (crab-eating macaques, Macaca fascicularis) at a quarantine facility in Reston, Virginia, began to succumb with rather frightening rapidity to an outbreak of a haemorrhagic fever.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“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 6,900 (including the county), there were only a few cases and a single death.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“One of the greatest ideas of mathematics is that all time series one is likely to encounter in nature can be described as a superposition of periodic functions.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“As epidemiologists, we are interested in equilibria because stable equilibria tell us when a system has attained stability -- or where it will, eventually, attain stability. Epidemics are 'extraordinary events'. The term 'outbreak', beloved of the popular media when commenting on epidemics, emphasises that we are dealing with a phenomenon that goes counter to 'business as usual'. Stable equilibria are nothing more than mathematical descriptions of states in which the system can settle again and attain a measure of normalcy.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“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.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“Vaccination has made smallpox extinct in the wild, as well as rinderpest, a relative of measles that affects cattle and buffalo, among others. Poliomyelitis, which has in its heyday killed and maimed millions of children and adults alike, is close to eradication, with fewer than 200 wild-type cases documented in 2020. Vaccines are some of the most effective public health interventions against infectious disease.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“The word 'vaccination' comes from vacca, the Latin word for 'cow'. This is a poignant recapitulation of the history of vaccines. The first vaccine properly so called had, as its active ingredient, the cowpox virus, a close relative of smallpox that however was much less likely to cause severe, disfiguring or lethal disease. Edward Jenner observed that milkmaids, who were often exposed to cowpox, suffered a relatively mild disease, but would be immune to the much more serious smallpox.

In an experiment that would unlikely pass muster in the modern world, he infected James Phipps, then an 8-year-old, with cowpox. He suffered a mild and transient illness, but when he was later exposed to scabs from a smallpox patient, he proved immune.

Unlike the earlier practice of variolation, which has been practised in late Song dynasty China that sought to induce the cutaneous form of smallpox, variola minor, to protect against the more severe forms of smallpox (variola major), Jenner's vaccination used a less pathogenic virus.
He relied on what would later be called 'antigenic similarity', but which was at the time hardly understood.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“higher antimicrobial loads will result in a lower total pathogenic load but also a lower involvement of the immune system and therefore less immunity in the long run (as indeed has been empirically demonstrated in a number of experiments summarised in a sweeping review by Benoun (2016)). Thus, while rapid and aggressive antimicrobial treatment is sometimes appropriate, the long-term absence of ensuing CD4+ immunity is its cost.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“MARV serves as a poignant example of the way a pathogen that is highly prevalent in its reservoir host population can hide safely without human notice, until in some unfortunate accident, hosts and vectors cross paths.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“Few diseases have had an impact on human evolution, culture and society on par with malaria. It is one of the oldest documented infectious diseases. Indeed, it has been hypothesised that the protective effect bestowed by a heterozygous sickle cell allele explains its survival to the modern day. As such, malaria has left its footprint on human evolution in a profound way few other diseases have.

Yet its true origins were the matter of considerable controversy. The clue is in the name – the prevailing theory until Ross's discovery was that malaria resulted from 'mala aria', that is, 'bad air'.
It took the advent of modern evidence-based medical science to challenge this 'miasma theory'. Ross's elucidation of the role of mosquitoes in the lifecycle of malaria has opened up a new subject for epidemiological consideration: the vector-borne disease.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“There is an unhelpful tendency to regard superspreaders – and events where superspreading has occurred – as anomalies out of the ordinary. This contributes relatively little to our understanding of infectious dynamics and is bound to exacerbate the stigmatisation of individuals, as it has e.g. during the early years of AIDS, when much sensationalistic and unjustified blame was laid at the feet of early HIV patient Gaetan Dugas (on which see McKay, 2014). Rather, superspreading is one 'tail' of a distribution prominent mainly because it is noticeable – statistical models predict that there are generally an equal number of 'greatly inferior spreaders' who are particularly ineffective in spreading the illness.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“Mary Mallon was born in 1869 in Cookstown, County Tyrone, then part of British-ruled Ireland. Like many of her countrymen, she immigrated to the United States at a young age, where she eventually found employment as a cook. During her lifetime, it was suspected that she has unintentionally (albeit perhaps negligently) infected over fifty people with typhoid.

Typhoid fever is a bacterial disease caused by gastrointestinal infection by Salmonella enterica serovar Typhi. In most patients, it causes an unpleasant but manageable disease that resolves fully. However, as many as one in twenty patients become chronic carriers, who continue to be infectious for their lifetimes. Mary Mallon was one of the unfortunate few who fell into that category. It is hypothesised today that she contracted typhoid at birth.

Her case, which involved prolonged quarantine on North Brother Island for almost half her life, raises complex moral and ethical questions about reconciling the interests of public health with the moral imperative to respect individual liberties and treat the sick (even if asymptomatic) with compassion.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“To those in charge of responding to an epidemic, from disaster management professionals through hospitals to governmental authorities, it is not merely the overall number of cases over the epidemic's lifetime that matters, but also how that number is distributed over time. Rapid epidemic spikes can overwhelm healthcare capacities and cause excess mortality from lack of care, both among the victims of the epidemic and from unaffected individuals who cannot avail themselves of adequate care due to acute overload of the hospital system. For this reason, we are interested in the maxima that we have explored previously.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python

“Any subject whose history ranges from pump handles on London's Broad Street, tide tables, naval gunfire and models of social segregation is bound to have rich parentage.
It took 'a village' to beget computational epidemiology: as a true multi-disciplinary subject, it evolved at the crossroads of mathematics, computation, statistics and medicine, with some contributions from systems biology, virology, microbiology, game theory, geography and perhaps even the social sciences.”
― Chris von Csefalvay, Computational Modeling of Infectious Disease: With Applications in Python