Presentamos la colección de modelos matemáticos de dinámica de enfermedades infecciosas

Presentamos la colección de modelos matemáticos de
dinámica de enfermedades infecciosas
   blogs.plos.org / everyone / 2020/02/20 / Math -disease-dynamics /

Editores de PLOS ONE                                                       20 de febrero de
                                                                           2020

En los últimos meses, las palabras "infección" y "brote" no han estado lejos de la mente de
nadie ya que nos hemos enfrentado a la aparición de un nuevo coronavirus, COVID-19. En
todo el mundo, se están realizando esfuerzos para controlar y limitar la propagación del
virus, y para encontrar formas de tratar a los infectados. A medida que vemos cómo se
desarrollan estos eventos, es evidente que todavía hay mucho que nosotros, como
comunidad global, aún no comprendemos sobre la dinámica de las enfermedades
infecciosas. Las formas en que se propagan las enfermedades son una preocupación de la
que todos tenemos interés en ー la investigación que ayuda a nuestra comprensión de las
enfermedades infecciosas puede influir en cada una de nuestras vidas.

Una comunidad distinta de investigadores que trabajan para comprender la dinámica de las
enfermedades infecciosas es la comunidad de modelos matemáticos, que consiste en
científicos de muchas disciplinas diferentes que se unen para abordar un problema común
mediante el uso de modelos matemáticos y simulaciones por computadora. Las
matemáticas pueden sonar como un héroe poco probable que nos ayude a superar una
epidemia mundial; sin embargo, no debemos subestimar las ideas que obtenemos al
estudiar la dinámica de las enfermedades infecciosas mediante el uso de ecuaciones que
describen variables fundamentales. Al abordar las enfermedades infecciosas desde una

                                                                                              1/8

perspectiva matemática, podemos identificar patrones y sistemas comunes en la función de
la enfermedad, y nos permite encontrar algunas de las estructuras subyacentes que rigen
los brotes y las epidemias.

Hoy en PLOS, estamos lanzando una colección de nuevos trabajos de investigación enviados
a una convocatoria de trabajos durante la segunda mitad de 2019 titulada "Modelado
matemático de la dinámica de enfermedades infecciosas", organizada por PLOS Biology ,
PLOS Computational Biology y PLOS ONE. El objetivo de esta colección es reunir diferentes
disciplinas como las matemáticas, la biología, la medicina y la física con el fin de arrojar luz
sobre el importante tema de cómo los modelos matemáticos pueden ayudarnos a
comprender la dinámica de las enfermedades infecciosas, y presentar esta investigación al
amplio público. de estas tres revistas y más allá. La acumulación de nuevas investigaciones
vitales en una colección integral será un recurso útil para comprender cómo operan las
enfermedades infecciosas y cómo podemos abordarlas en tiempo real y en el futuro.

En PLOS seguimos comprometidos con nuestra misión principal de Acceso Abierto ー
asegurando que la ciencia esté lo más ampliamente disponible posible, y no bloqueada
detrás de los muros de pago. Esto es especialmente importante en escenarios de brotes,
como la actual epidemia de COVID-19, donde es fundamental que cualquier investigación
nueva y relevante sea fácilmente accesible en todo el mundo, inmediatamente al momento
de la publicación.

Varios de los documentos de esta
colección presentan nuevos
métodos que pueden utilizarse en
una variedad de escenarios. Por
ejemplo, Patel y Sprouge
desarrollaron un nuevo estimador
para predecir el número básico de
reproducción R 0 , que es el número
esperado de células huésped
infectadas por una sola célula
infectada. Esto se puede utilizar, por
ejemplo, para comprender las
primeras etapas de las infecciones
por VIH y para evaluar la efectividad
de varias terapias.
                                                Nuevo coronavirus SARS-CoV-2 NIAID CC-BY
Si dos especies patógenas, cepas o
clones no interactúan, ¿podemos estimar la proporción de huéspedes coinfectados como el
producto simple de las prevalencias individuales? Un artículo en PLOS Biology de Frédéric
Hamelin, Nik Cunniffe y colaboradores muestra que esta suposición es falsa; incluso si los
patógenos no interactúan, la muerte de los hospedadores coinfectados hace que las
                                                                                                   2/8

prevalencias netas de patógenos individuales disminuyan simultáneamente. Los autores
reinterpretan los datos de estudios previos en consecuencia.

Los brotes de paperas inusualmente grandes en los Estados Unidos en 2016 y 2017
plantearon preguntas sobre el alcance de la circulación de las paperas y la relación entre
estos y brotes anteriores. En este artículo de PLOS Biology , Shirlee Wohl, Pardis Sabeti y sus
coautores combinaron datos epidemiológicos de investigaciones de salud pública con
análisis de secuencias del genoma completo del virus de las paperas de 201 individuos
infectados. Esto les permitió reconstruir enlaces de transmisión de paperas no evidentes a
partir de enfoques más tradicionales y también reveló conexiones entre brotes de paperas
aparentemente no relacionados.

Endo y sus colegas presentan un modelo de un fenómeno con el que todos podemos
relacionarnos, pero que aún no se comprende bien: la propagación de la infección dentro
del hogar. Modelaron las finas estructuras de la vida familiar para comprender cómo las
enfermedades típicamente ingresan y se propagan por el hogar. Sus hallazgos respaldan la
idea de que los niños son los culpables más probables de llevar la enfermedad al hogar, y
mostraron que existe un alto nivel de transmisión dentro de las generaciones, así como
entre la madre y el niño.

Rotavirus, the leading cause of diarrhea
globally in children under 5, shows a biennial
pattern of emergence in the US, while in
many other high-income countries it exhibits
an annual pattern. Ai and colleagues
modelled the effect that higher vaccine
coverage may have on this phenomenon, and
found that increasing vaccine coverage from
the current 70-75% to 85% would not only
reduce the number of rotavirus cases, but
also shift occurance to a more predictable
annual epidemic pattern.

Two of the papers published in the collection
are concerned with malaria. Kim and
colleagues modelled the effectiveness of                       FluShot NIAID CC-BY
relapse control methods for Plasmodium
vivax, finding that current vector control methods may have a negative effect on controlling
disease prevalence, but that a shift towards control at a higher vector control level may be
more efficient. Meanwhile, Wang and colleagues have constructed a stacking model for

                                                                                                  3/8

malaria prediction by combining two traditional time series models and two deep learning
methods. Utilising malaria incidence data from Yunnan Province, China, they find that the
ensemble architecture outperforms each of the sub-structure models in predicting malaria
cases.

                 Predicted dengue importations for August 2015 pone.0225193 CC-
                                               BY

There are two papers in the collection that look at improving prediction of dengue
infections. Leibig and colleagues present a network model of how international air travel
can affect the spread of dengue across the world. By modelling the number of dengue-
infected passengers arriving at various airports each month, the authors were able to study
how dengue may be imported into different countries, and which routes would be the most
likely for dengue-infected passengers to arrive by. Secondly, Liu and colleagues developed a
model for predicting the spread of dengue infections that incorporates climate factors such
as mean temperature, relative humidity and precipitation and applied this to data from
dengue infections in Guangzhou, China, in order to help inform best practices in the early
stages of a dengue outbreak.

The development of diseases can be influenced by personal factors such as age, which two
of the papers in the collection address. Ku and Dodd developed a model for accounting for
population aging when looking at tuberculosis incidence, as the impact of demographic
change on disease forecasting is still not well understood. They applied the model to
historical data of TB cases in Taiwan from 2005-2018, and used this to forecast what the
incidence may look like until 2035. On the other end of the age spectrum, Rostgaard and
colleagues used a Markov model to study the relationship between Epstein-Barr virus and
infectious mononucleosis. Most people are typically infected with Epstein-Barr virus in early
childhood, while infectious mononucleosis can sometimes follow in adolescence or later in
life. The authors developed a statistical model to probe some of the uncertainties
surrounding the origin and dynamics of infectious mononucleosis.

                                                                                                4/8

Some of the papers in the collection address new and emerging diseases. Dodero-Rojas and
colleagues used the SEIR model to study the last three Chikungunya outbreaks in Rio de
Janeiro, Brazil, and estimated their respective Basic Reproduction Numbers, R0. They also
expanded their findings to include predictions for the Mayaro virus, which is an emerging
disease in South America, and found that it has the possibility to become an epidemic
disease in Rio de Janeiro.

                                  Aedes Mosquito NIAID CC-BY

The ability to accurately forecast disease patterns is crucial for ensuring that the right
resources are in place to handle outbreaks. Morbey and colleagues looked at seasonal
patterns in respiratory disease in England, and found that although syndromic indicators
were affected by the timing of the peaks in seasonal disease, the demand for hospital beds
was the highest on either 29th or 30th December, regardless of the timing of the syndromic
peaks. Asadgol and colleagues also addressed seasonal patterns, this time in cholera in Iran,
and predicted the effect of climate change on cholera incidence from 2020-2050 using an
artificial neural network.

Given the interdisciplinary nature of the topic, we are grateful to countless authors,
reviewers, Academic Editors and Guest Editors for making this collection a reality. We are
especially grateful to our Guest Editor team, Konstantin Blyuss (University of Sussex), Sara
Del Valle (Los Alamos National Laboratory), Jennifer Flegg (University of Melbourne), Louise
Matthews (University of Glasgow) and Jane Heffernan (York University) for curating the
collection. While 14 papers are included in this collection today, we’ll keep adding new
papers as they are published, so please keep checking back for updates.

Guest Editor Konstantin Blyuss sums up the importance of this collection: “A recent and
ongoing outbreak of coronavirus COVID-19 has highlighted the enormous significance of
mathematical models for understanding the dynamics of infectious diseases and developing
appropriate strategies for mitigating them. Mathematical models have helped identify the

                                                                                                5/8

important factors affecting the spread of this infection both globally, and locally using
country-specific information. They have also elucidated the effectiveness of different
containment strategies and provided quantitative measures of disease severity”.

About the Guest Editors:

Konstantin Blyuss
Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational
Biology

Konstantin Blyuss is a Reader in the Department of Mathematics at
the University of Sussex, UK. He obtained his PhD in applied
mathematics at the University of Surrey, which was followed by
PostDocs at Universities of Exeter and Oxford. Before coming to
Sussex in 2010, he was a Lecturer in Complexity at the University of Bristol. His main
research interests are in the area of dynamical systems applied to biology, with particular
interest in modelling various aspects of epidemiology, dynamics of immune responses and
autoimmunity, as well as understanding mechanisms of interactions between plants and
their pathogens

Sara del Valle
Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational
Biology

Dr. Sara Del Valle is a scientist and deputy group leader in the
Information Systems and Modeling Group at Los Alamos National
Laboratory. She earned her Ph.D. in Applied Mathematics and
Computational Science in 2005 from the University of Iowa. She
works on developing, integrating, and analyzing mathematical, computational, and statistical
models for the spread of infectious diseases such as smallpox, anthrax, HIV, influenza,
malaria, Zika, Chikungunya, dengue, and Ebola. Most recently, she has been investigating
the role of heterogeneous data streams such as satellite imagery, Internet data, and climate
on detecting, monitoring, and forecasting diseases around the globe. Her research has
generated new insights on the impact of behavioral changes on diseases spread as well as
the role of non-traditional data streams on disease forecasting.

Jennifer Flegg                                                                                 6/8

Jennifer Flegg
Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational
Biology

Jennifer Flegg is a Senior Lecturer and DECRA fellow in the School
of Mathematics and Statistics at the University of Melbourne. Her
research focuses on mathematical biology in areas such as wound
healing, tumour growth and epidemiology. She was awarded a PhD
in 2009 from Queensland University of Technology on mathematical modelling of tissue
repair. From 2010 – 2013, she was at the University of Oxford developing statistical models
for the spread of resistance to antimalarial drugs. From 2014 – April 2017 she was a Lecturer
in the School of Mathematical Sciences at Monash University. In May 2017 she joined the
School of Mathematics and Statistics at the University of Melbourne as a Senior Lecturer in
Applied Mathematics.

Louise Matthews
Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational
Biology

Louise Matthews is Professor of Mathematical Biology and
Infectious Disease Ecology at the Institute of Biodiversity, Animal
Health and Comparative Medicine (BAHCM) at the University of
Glasgow. She holds a degree and PhD in mathematics and has
over 20 years research experience as an epidemiologist, with a particular focus on diseases
of veterinary and zoonotic importance. Her current interests include a focus on drug
resistance; antibiotic resistance in livestock; the community and the healthcare setting;
anthelminthic resistance in livestock; and drug resistance in African Animal
Trypanosomiasis. She is also interested in the integration of economic and epidemiological
approaches such as game theory to understand farmer behaviour and micro-costing
approaches to promote adoption of measures to reduce antibiotic resistance.

Jane Heffernan
Guest Editor, PLOS ONE, PLOS Biology, and PLOS Computational
Biology

Jane Heffernan is a Professor in the Department of Mathematics
and Statistics at York University, and York Research Chair (Tier II).
She is also the Director of the Centre for Disease Modelling (CDM),
and serves on the Board of Directors of the Canadian Applied and
Industrial Mathematics Society (CAIMS). She is also very active in the Society for
Mathematical Biology (SMB). Dr. Heffernan’s research program centers on understanding
                                                                                                7/8

the spread and persistence of infectious diseases. Her Modelling Infection and Immunity
Lab focuses on the development of new biologically motivated models of infectious diseases
(deterministic and stochastic) that describe pathogen dynamics in-host (mathematical
immunology) and in a population of hosts (mathematical epidemiology), as well as models
in immuno-epidemiology, which integrate the in-host dynamics with population level
models. More recently, Heffernan is focusing on applying mathematics and modelling to
studying pollinator health and disease biology.

Imagen destacada: Spencer J. Fox, CC0

                                                                                             8/8