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 2 months ago

cpp

10.62 score 2 stars 3 dependents 444 scripts 2.0k downloads

hhh4addon - Extensions to endemic-epidemic timeseries modeling from package surveillance

Extending surveillance::hhh4 to allow for distributed lags, solutions for longterm prediction and (periodically) stationary moments.

Last updated 4 months ago

4.91 score 6 stars 45 scripts

HIDDA.forecasting - Forecasting Based on Surveillance Data

The Handbook of Infectious Disease Data Analysis ("HIDDA") contains a chapter on "Forecasting Based on Surveillance Data". The R package 'HIDDA.forecasting' provides the data and code to reproduce results from the two applications described in that chapter (see the corresponding vignettes): Univariate forecasting of Swiss ILI counts using 'forecast', 'glarma', 'surveillance' and 'prophet', and an age-stratified analysis of norovirus gastroenteritis in Berlin using the multivariate time-series model implemented in surveillance::hhh4().

Last updated 1 years ago

forecastinginfectious-diseasestime-series

4.02 score 3 stars 3 scripts

hhh4contacts - Age-Structured Spatio-Temporal Models for Infectious Disease Counts

Meyer and Held (2017) <doi:10.1093/biostatistics/kxw051> present an age-structured spatio-temporal model for infectious disease counts. The approach is illustrated in a case study on norovirus gastroenteritis in Berlin, 2011-2015, by age group, city district and week, using additional contact data from the POLYMOD survey. This package contains the data and code to reproduce the results from the paper, see 'demo("hhh4contacts")'.

Last updated 3 months ago

3.28 score 19 scripts 270 downloads

hhh4ZI - Zero-Inflated Endemic-Epidemic Count Time Series

Extending 'surveillance::hhh4()' for zero-inflated infectious disease counts as described in Lu and Meyer (2023) <doi:10.1002/bimj.202100408>. This package is a partial fork of 'surveillance' and intended as a proof of concept for this model extension. It will be merged with other extensions in the future.

Last updated 2 months ago

2.18 score 3 stars 3 scripts

twins - Fit a Two-Component Epidemic Count Model using MCMC

Fits a negative-binomial epidemic model as described in Held et al. (2006) <doi:10.1093/biostatistics/kxj016> to a univariate time series of counts. The "quick & dirty" C++ code implementing 'algo.twins()' was previously distributed as part of the R package 'surveillance' (up to version 1.23.1) but is now archived in this stand-alone package.

Last updated 2 months ago

cpp

1.60 score