pomp - Statistical Inference for Partially Observed Markov Processes

Tools for data analysis with partially observed Markov process (POMP) models (also known as stochastic dynamical systems, hidden Markov models, and nonlinear, non-Gaussian, state-space models). The package provides facilities for implementing POMP models, simulating them, and fitting them to time series data by a variety of frequentist and Bayesian methods. It is also a versatile platform for implementation of inference methods for general POMP models.

Last updated 18 days ago

abcb-splinedifferential-equationsdynamical-systemsiterated-filteringlikelihoodlikelihood-freemarkov-chain-monte-carlomarkov-modelmathematical-modellingmeasurement-errorparticle-filtersequential-monte-carlosimulation-based-inferencesobol-sequencestate-spacestatistical-inferencestochastic-processestime-series

11.64 score 114 stars 4 packages 1.3k scripts 2.6k downloads

COVID19 - COVID-19 Data Hub

Unified datasets for a better understanding of COVID-19.

Last updated 21 days ago

2019-ncovcoronaviruscovid-19covid-datacovid19-data

10.45 score 251 stars 283 scripts 614 downloads

surveillance - Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic Phenomena

Statistical methods for the modeling and monitoring of time series of counts, proportions and categorical data, as well as for the modeling of continuous-time point processes of epidemic phenomena. The monitoring methods focus on aberration detection in count data time series from public health surveillance of communicable diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the negative binomial GLR-CUSUM method of Hoehle and Paul (2008) <doi:10.1016/j.csda.2008.02.015>. A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures is given by Salmon et al. (2016) <doi:10.18637/jss.v070.i10>. For the retrospective analysis of epidemic spread, the package provides three endemic-epidemic modeling frameworks with tools for visualization, likelihood inference, and simulation. hhh4() estimates models for (multivariate) count time series following Paul and Held (2011) <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed population, e.g, epidemics across farms or networks, as a multivariate point process as proposed by Hoehle (2009) <doi:10.1002/bimj.200900050>. twinstim() estimates self-exciting point process models for a spatio-temporal point pattern of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.

Last updated 22 days ago

9.36 score 11 stars 3 packages 448 scripts 1.6k downloads

cholera - Amend, Augment and Aid Analysis of John Snow's Cholera Map

Amends errors, augments data and aids analysis of John Snow's map of the 1854 London cholera outbreak.

Last updated 5 hours ago

choleradata-visualizationdatasetsepidemiologyjohn-snowpublic-healthtriangulation-delaunayvoronoivoronoi-polygons

9.32 score 135 stars 95 scripts 395 downloads

cfr - Estimate Disease Severity and Case Ascertainment

Estimate the severity of a disease and ascertainment of cases, as discussed in Nishiura et al. (2009) <doi:10.1371/journal.pone.0006852>.

Last updated 1 months ago

case-fatality-rateepidemic-modellingepidemiologyepiversehealth-outcomesoutbreak-analysissdg-3

8.09 score 13 stars 32 scripts 320 downloads

inctools - Incidence Estimation Tools

Tools for estimating incidence from biomarker data in cross- sectional surveys, and for calibrating tests for recent infection. Implements and extends the method of Kassanjee et al. (2012) <doi:10.1097/EDE.0b013e3182576c07>.

Last updated 4 years ago

biomarkersbiostatisticsepidemiologyhivincidenceincidence-estimationincidence-inferenceinfectious-diseasesstatistics

6.51 score 6 stars 27 scripts 214 downloads