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A Home Flu "Kit" to Empower Individuals and Families for Pandemic Flu
Stan Finkelstein
Center for Engineering Systems Fundamentals
Engineering Systems Division
Massachusetts Institute of Technology
& Harvard-MIT Division of Health Sciences & Technology
Shiva Prakash
Center for Engineering Systems Fundamentals
Engineering Systems Division
Massachusetts Institute of Technology
& Harvard-MIT Division of Health Sciences & Technology
James McDevitt
Department of Environmental Health
Exposure, Epidemiology and Risk Program
Harvard School of Public Health
Richard C. Larson
Center for Engineering Systems Fundamentals
Engineering Systems Division
Massachusetts Institute of Technology
& Harvard-MIT Division of Health Sciences & Technology
November 2009
Abstract:
Background
When a flu pandemic occurs, it can overwhelm the capacities of our hospitals, clinics, nursing homes, and emergency services. Most of the stricken will have to be cared for at home, and there is strong evidence that in-home caregivers bear a disproportionate risk of becoming infected. We focus on simple steps that in-home caregivers can take to reduce the chances that they and other household members will become infected from the stricken individual being cared for in the home.
Methods
We examined the feasibility that a portfolio of non-pharmaceutical remedies, easily implemented in the home would reduce the local spread of illness.
Results
Personal hygiene, common masks, and technologies including air filters and ultraviolet light each offer incremental benefits, and in combination are expected to reduce some of the risk that caregivers and other household members become infected.
Conclusions
In pandemics and even seasonal epidemics, seemingly small steps could literally mean the difference between life and death, especially for in-home caregivers at high risk.
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Engineering Responses to Pandemics
Richard C. Larson
Massachusetts Institute of Technology
Karima R. Nigmatulina
Massachusetts Institute of Technology
Astract:
Focusing on pandemic influenza, this chapter approaches
the planning for and
response to such a major worldwide health event as a complex
engineering systems problem. Action-oriented analysis of
pandemics requires a broad inclusion of academic disciplines
since no one domain can cover a significant fraction of
the problem. Numerous research papers and action plans have
treated pandemics as purely medical happenings, focusing
on hospitals, health care professionals, creation and distribution
of vaccines and anti-virals, etc. But human behavior with
regard to hygiene and social distancing constitutes a first-order
partial brake or control of the spread and intensity of
infection. Such behavioral options are “non-pharmaceutical
interventions.” (NPIs) The chapter employs simple
mathematical models to study alternative controls of infection,
addressing a well-known parameter in epidemiology, R0, the
“reproductive number,” defined as the mean number
of new infections generated by an index case. Values of
R0 greater than 1.0 usually indicate that the infection
begins with exponential growth, the generation-to-generation
growth rate being R0. R0 is broken down into constituent
parts related to the frequency and intensity of human contacts,
both partially under our control. It is suggested that any
numerical value for R0 has little meaning outside the social
context to which it pertains. Difference equation models
are then employed to study the effects of heterogeneity
of population social contact rates, the analysis showing
that the disease tends to be driven by high frequency individuals.
Related analyses show the futility of trying geographically
to isolate the disease. Finally, the models are operated
under a variety of assumptions related to social distancing
and changes in hygienic behavior. The results are promising
in terms of potentially reducing the total impact of the
pandemic.
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White Paper On Novel H1N1: Prepared
for the MIT Center for Engineering Systems Fundamentals
(Revised July 27, 2009)
John M. Barry
Distinguished Scholar, Tulane University Center for Bioenvironmental
Research
Member, Advisory Board, MIT Center for Engineering Systems
Fundamentals
Astract:
This paper is an historical and policy primer for one to
prepare for a severe flu pandemic - which is virtually guaranteed
to happen at some time in the future. The paper provides
actionable knowledge, gleaned from past flu pandemics and
from recent science, to reduce the chance of you and your
loved ones from contracting the flu. The paper discusses
both the new novel H1N1 flu virus and the more lethal H5
N1 ("bird flu") virus. In discussing the future
of H1N1, the author says, "Three of the preceding four
pandemics, 1889, 1918, and 1957, show clear evidence of
some fairly intense but sporadic initial local outbreaks
scattered around the world. The novel H1N1 virus seems thus
far to be following the pattern of those three pandemics,
and it seems highly likely that it will return in full flower."
The author projects that a full fledged global pandemic
could cut global GDP by up to 4 to 6 percent, and that companies
must now prepare for supply chain disruptions, even if only
the milder H1N1 becomes the prevalent flu. An individual's
behavioral changes with social distancing and hygienic steps
can dramatically reduce the chance of contracting the flu.
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Planning for a Flu Pandemic: Policies
to Empower Individuals and Families
Shiva Prakash
Center for Engineering Systems Fundamentals
Engineering Systems Division
Massachusetts Institute of Technology
Stan Finkelstein
Engineering Systems Division
Harvard-MIT Division of Health Sciences & Technology
Massachusetts Institute of Technology
Richard C. Larson
Center for Engineering Systems Fundamentals
Engineering Systems Division
Massachusetts Institute of Technology
Astract:
No one can predict how much sickness and loss of life will
result if and when the next influenza pandemic occurs. Experts
agree, the issue is not if it will occur, but when. Whatever
that time is, such pandemic flu would likely overwhelm the
capacities of our hospitals, clinics and emergency services.
Most people ill with the flu will have to be cared for at
home by family members and other trusted caregivers.
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Revisiting R0,
the Basic Reproductive Number for Pandemic Influenza
Richard C. Larson
Center for Engineering Systems Fundamentals
Engineering Systems Division
Massachusetts Institute of Technology
Astract: This paper focuses
on a fundamental input parameter for most existing mathematical
models of pandemic influenza, the ‘basic reproductive
number R0,’ defined to be the
mean number of new influenza infections created by a newly
infected person in a population of all susceptible people.
We argue that R0 is limited in policy
and scientific value as is any single parameter attempting
to characterize a complex probabilistic process. In particular,
we demonstrate by simple logic that R0
does not exist as a separate ‘constant of a particular
influenza,’ but rather its value is determined by
social context and behavioral patterns as well as by the
“physics’’ of the influenza virus. To
the extent that R0 is useful, it is
best viewed as an output of a modeling analysis, not an
input. But with R0 being the mean
of a random variable, much more information is contained
in the entire probability distribution. With this view,
we show – again by simple arguments – that R0
can be greater than 1.0 and still, contrary to popular belief,
the probability of an exponentially growing pandemic may
be arbitrarily small. Finally, we show that attempts to
estimate R0 from data of previous
pandemics is fraught with methodological complexities, due
primarily to heterogeneities in the population that cause
super-spreaders and socially active people to be the first
propagators of the disease. Unless one is careful, statistical
estimates of R0 based on early exponential
growth of reported cases may be significantly upwardly biased.
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Stopping Pandemic Flu: Government
and Community Interventions in a Multi-Community Model
Karima R. Nigmatulina
Operations Research
Center
Massachusetts Institute of Technology
Richard C. Larson
Department of Civil and
Environmental Engineering
Engineering Systems Division
Massachusetts Institute of Technology
Astract: Focusing on mitigation
strategies for global pandemic influenza, we use elementary
mathematical models to evaluate the implementation and timing
of intervention strategies such as travel restrictions,
vaccination, social distancing and improved hygiene. A spreadsheet
model of infection spread between several linked heterogeneous
communities is based on analytical calculations and Monte
Carlo simulations. Since human behavior will likely change
during the course of a pandemic, thereby altering the dynamics
of the disease, we incorporate a feedback parameter into
our model to reflect altered behavior. Our results indicate
that while a flu pandemic could be devastating; there are
coping methods that when implemented quickly and correctly
can significantly mitigate the severity of a global outbreak.
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Pandemic Flu: Yes, We Can Do Something
About It!
Richard C. Larson
Center for Engineering
Systems Fundamentals
Massachusetts Institute of Technology
Astract: The emergence of
influenza with virulence comparable to the famous 1918-1919
“Spanish Flu” has the potential to kill hundreds
of millions of people worldwide. Should we find ourselves
being forced to ‘live with the flu,’ it is imperative
that we recognize that there are things that we can do –
many simple – that may decrease the chance of our
loved ones, our co-workers and ourselves becoming infected
with the flu. The key is to decrease the number of new infections
created by each newly infected person. And this relates
to mathematical modeling of the disease, a very simple example
of which is shown here.
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Simple Models of Influenza Progression
within a Heterogeneous Population
Richard C. Larson
Center for Engineering
Systems Fundamentals
Engineering Systems Division
Department of Civil and Environmental Engineering
Massachusetts Institute of Technology
Astract: The focus of this
‘OR framing paper’ is to introduce the OR community
to the need for new mathematical modeling of an influenza
pandemic and its control. By reviewing relevant history
and literature, one key concern that emerges relates to
how a population’s heterogeneity may affect disease
progression. Another is to explore within a modeling framework
‘social distancing’ as a disease progression
control method, where social distancing refers to steps
aimed at reducing the frequency and intensity of daily human
to human contacts. To depict social contact behavior of
a heterogeneous population susceptible to infection, a non-homogeneous
probabilistic mixing model is developed. Partitioning the
population of susceptibles into subgroups, based on frequency
of daily human contacts and infection propensities, a stylistic
difference equation model is then developed depicting the
day-to-day evolution of the disease. This simple model is
then used to develop a preliminary set of results. Two key
findings are (1) early exponential growth of the disease
may be dominated by susceptibles with high human contact
frequencies and may not be indicative of the general population’s
susceptibility to the disease; and (2) social distancing
may be an effective non-medical way to limit and perhaps
even eradicate the disease. Much more decision-focused research
needs to be done before any of these preliminary findings
may be used in practice.
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