mcp: An R Package for Regression With Multiple Change Points

Lindeløv, Jonas Kristoffer

doi:10.31219/osf.io/fzqxv

Cited by 111 publications

(130 citation statements)

References 37 publications

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“…The number of change points in mean and variance of the resulting D 50 time series were detected using the changepoint package in R (version 2.3.1, [53]) using the PELT algorithm and CROPS penalty. Locations of the identi ed number of change points were detected using Dirichlet priors (α = number of identi ed change points) in the mcp R package (version 0.3.0, [54]).…”

Section: Methodsmentioning

confidence: 99%

Influence of thermal stratification and storms on acoustic telemetry detection efficiency: a year-long test in the US Southern Mid-Atlantic Bight

O’Brien

Secor

2020

Preprint

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Background: The detection efficiency of ultrasonic transmitters is seasonally variable, requiring long-term studies to evaluate key environmental features that mask, alter speed, bend, or reflect transmissions. The US Southern Mid-Atlantic Bight shelf is characterized by a strong summer thermocline capping remnant winter water, known as the Cold Pool, and a well-mixed water column in other seasons. To investigate the effects of interactions between temperature stratification and storm-induced noise on transmission detectability, we conducted a year-long range test in the bottom waters of the US Southern Mid-Atlantic Bight. We used generalized additive models and cross-validation to develop and evaluate a predictive model of detection efficiency and visualize variability in detection distance throughout the year of deployment.Results: The most-predictive model utilized the interaction between temperature stratification and ambient noise, predicting that stratification results in a 54% increase in detectability and 56% increase in detection distance. The model had an overall error rate of 16.3% and an 18.7% error at a distance of 800 m, predicting 9% detectability when the water column was not stratified and 63% when the difference between surface and bottom temperatures was greater than 4.6 °C. The distance at 50% detectability increased with the formation of the Cold Pool during spring, shifting from 559 ± 149 m to 872 ± 81 m over 3 days. All seasons were associated with storm-induced reductions in overall detectability and distance at 50% detectability.Conclusion: Thermal stratification within the Southern Mid-Atlantic Bight increases bottom water ultrasonic transmitter detection distance and reduces the impact of surface noise associated with large storms. This effect leads to a seasonal increase in detection distance from the late-spring through the summer. To our knowledge, this study is the first to report and quantify an increase in detection range as a result of temperature stratification, likely due to placing transmitters and receivers on the same side of a strong thermocline.

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Section: Methodsmentioning

confidence: 99%

Influence of thermal stratification and storms on acoustic telemetry detection efficiency: a year-long test in the US Southern Mid-Atlantic Bight

O’Brien

Secor

2020

Preprint

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“…Once the time series is inferred, we assess the timing and slope of changes in R 0 by using linear changepoint models (Lindeløv 2020) . The null model corresponds to a linear decrease between two plateaus corresponding to the baseline value at the start of the epidemic and a low value after the implementation of NPIs.…”

Section: Data and Inferencementioning

confidence: 99%

Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland

Lemaitre

Perez-Saez

Azman

et al. 2020

Preprint

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Following the rapid dissemination of COVID-19 cases in Switzerland, large-scale non- pharmaceutical interventions (NPIs) were implemented by the cantons and the federal government between February 28 and March 20. Estimates of the impact of these interventions on SARS-CoV-2 transmission are critical for decision making in this and future outbreaks. We here aim to assess the impact of these NPIs on disease transmission by estimating changes in the basic reproduction number (R0) at national and cantonal levels in relation to the timing of these NPIs. We estimate the time- varying R0 nationally and in twelve cantons by fitting a stochastic transmission model explicitly simulating within hospital dynamics. We use individual-level data of >1,000 hospitalized patients in Switzerland and public daily reports of hospitalizations and deaths. We estimate the national R0 was 3.15 (95% CI: 2.13-3.76) at the start of the epidemic. Starting from around March 6, we find a strong reduction in R0 with an 85% median decrease (95% quantile range, QR: 83%-90%) to a value of 0.44 (95% QR: 0.27-0.65) in the period of March 29-April 5. At the cantonal-level R0 decreased over the course of the epidemic between 71% and 94%. We found that reductions in R0 were synchronous with changes in mobility patterns as estimated through smartphone activity, which started before the official implementation of NPIs. We found that most of the reduction of transmission is due to behavioural changes as opposed to natural immunity, the latter accounting for only about 3% of the total reduction in effective transmission. As Switzerland considers relaxing some of the restrictions of social mixing, current estimates of R0 well below one are promising. However most of inferred transmission reduction was due to behaviour change (<3% due to natural immunity buildup), with an estimated 97% (95% QR: 96.6%-97.2%) of the Swiss population still susceptible to SARS-CoV-2 as of April 24. These results warrant a cautious relaxation of social distance practices and close monitoring of changes in both the basic and effective reproduction numbers.

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“…The time horizon for the analysis, t h , was selected based on the availability of data at the time of writing and also because it yielded a four-month long study period. All other dates for the theoretical model (t DP, t LP, t AP, t rP and t RP ) were estimated by applying a multiple change points detection method [68] and fitting the seven-phase model described above to the crime difference, ND(n). The result was then used to estimate the six indicators that were also introduced in the previous section.…”

Section: M1-city (China)mentioning

confidence: 99%

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

et al. 2020

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This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

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mcp: An R Package for Regression With Multiple Change Points

Cited by 111 publications

References 37 publications

Influence of thermal stratification and storms on acoustic telemetry detection efficiency: a year-long test in the US Southern Mid-Atlantic Bight

Influence of thermal stratification and storms on acoustic telemetry detection efficiency: a year-long test in the US Southern Mid-Atlantic Bight

Assessing the impact of non-pharmaceutical interventions on SARS-CoV-2 transmission in Switzerland

Measuring the resilience of criminogenic ecosystems to global disruption: A case-study of COVID-19 in China

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