7,337 research outputs found
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico
epidemiological modelling in predicting the dynamics of infectious diseases to
inform health policy and decision makers about suitable prevention and
containment strategies. Work in this setting involves solving challenging
inference and control problems in individual-based models of ever increasing
complexity. Here we discuss recent breakthroughs in machine learning,
specifically in simulation-based inference, and explore its potential as a
novel venue for model calibration to support the design and evaluation of
public health interventions. To further stimulate research, we are developing
software interfaces that turn two cornerstone COVID-19 and malaria epidemiology
models COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria
(https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling
efficient interpretable Bayesian inference within those simulators
Epidemic modelling by ripple-spreading network and genetic algorithm
Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results
Efficient detection of contagious outbreaks in massive metropolitan encounter networks
Physical contact remains difficult to trace in large metropolitan networks,
though it is a key vehicle for the transmission of contagious outbreaks.
Co-presence encounters during daily transit use provide us with a city-scale
time-resolved physical contact network, consisting of 1 billion contacts among
3 million transit users. Here, we study the advantage that knowledge of such
co-presence structures may provide for early detection of contagious outbreaks.
We first examine the "friend sensor" scheme --- a simple, but universal
strategy requiring only local information --- and demonstrate that it provides
significant early detection of simulated outbreaks. Taking advantage of the
full network structure, we then identify advanced "global sensor sets",
obtaining substantial early warning times savings over the friends sensor
scheme. Individuals with highest number of encounters are the most efficient
sensors, with performance comparable to individuals with the highest travel
frequency, exploratory behavior and structural centrality. An efficiency
balance emerges when testing the dependency on sensor size and evaluating
sensor reliability; we find that substantial and reliable lead-time could be
attained by monitoring only 0.01% of the population with the highest degree.Comment: 4 figure
A dynamic model of transmission and elimination of peste des petits ruminants in Ethiopia
Peste des petits ruminants (PPR), a devastating viral disease of sheep and goats, has been targeted by the global community for eradication within the next 15 years. Although an efficacious attenuated live vaccine is available, the lack of knowledge about the transmission potential of PPR virus (PPRV) may compromise eradication efforts. By fitting a metapopulation model simulating PPRV spread to the results of a nationwide serological survey in Ethiopia, we estimated the level of viral transmission in an endemic setting and the vaccination coverage required for elimination. Results suggest that the pastoral production system as a whole acts as a viral reservoir, from which PPRV spills over into the sedentary production system, where viral persistence is uncertain. Estimated levels of PPRV transmission indicate that viral spread could be prevented if the proportion of immune small ruminants is kept permanently above 37% in at least 71% of pastoral village populations. However, due to the high turnover of these populations, maintaining the fraction of immune animals above this threshold would require high vaccine coverage within villages, and vaccination campaigns to be conducted annually. Adapting vaccination strategies to the specific characteristics of the local epidemiological context and small ruminant population dynamics would result in optimized allocation of limited resources and increase the likelihood of PPR eradication
Non-systemic transmission of tick-borne diseases: a network approach
Tick-Borne diseases can be transmitted via non-systemic (NS) transmission.
This occurs when tick gets the infection by co-feeding with infected ticks on
the same host resulting in a direct pathogen transmission between the vectors,
without infecting the host. This transmission is peculiar, as it does not
require any systemic infection of the host. The NS transmission is the main
efficient transmission for the persistence of the Tick-Borne Encephalitis virus
in nature. By describing the heterogeneous ticks aggregation on hosts through a
\hyphenation{dynamical} bipartite graphs representation, we are able to
mathematically define the NS transmission and to depict the epidemiological
conditions for the pathogen persistence. Despite the fact that the underlying
network is largely fragmented, analytical and computational results show that
the larger is the variability of the aggregation, and the easier is for the
pathogen to persist in the population.Comment: 15 pages, 4 figures, to be published in Communications in Nonlinear
Science and Numerical Simulatio
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