epysurv.metrics package

Submodules

epysurv.metrics.outbreak_detection module

epysurv.metrics.outbreak_detection.ghozzi_case_score(prediction_result: pandas.core.frame.DataFrame) → float

Evalutes the performance of an outbreak detection.

Using the following formula: sum(p[t] * c[t] - (1 - p[t]) * c[t] - (p[t] != o[t]) * e[t] for t in timeseries) / sum(c) p: alarm c: count of outbreak cases o: outbreak e: endemic cases

Parameters

prediction_result – Dataframe containing the columns “alarm”, “outbreak” and “outbreak_cases”

Returns

A maximum score of 1.

epysurv.metrics.outbreak_detection.ghozzi_score(prediction_result: pandas.core.frame.DataFrame) → float

Evalutes the performance of an outbreak detection.

Using the following formula: sum(p[t] * c[t] - (1 - p[t]) * c[t] - (p[t] != o[t]) * mean(c) for t in timeseries) / sum(c) p: alarm c: count of outbreak cases o: outbreak

Parameters

prediction_result – Dataframe containing the columns “alarm”, “outbreak” and “outbreak_cases”

Returns

A maximum score of 1.

Module contents

epysurv.metrics.ghozzi_score(prediction_result: pandas.core.frame.DataFrame) → float

Evalutes the performance of an outbreak detection.

Using the following formula: sum(p[t] * c[t] - (1 - p[t]) * c[t] - (p[t] != o[t]) * mean(c) for t in timeseries) / sum(c) p: alarm c: count of outbreak cases o: outbreak

Parameters

prediction_result – Dataframe containing the columns “alarm”, “outbreak” and “outbreak_cases”

Returns

A maximum score of 1.