### Abstract

Mathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing. Trachoma control programs offer an opportunity to test this hypothesis, as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020. Local programs may benefit if a single survey could reveal whether infection was headed towards elimination. Using data from a previously-published 2009 survey, we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed. We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation. Model selection by Akaike Information Criteria (AIC_{c}) suggested the exponential distribution had the most parsimonious fit to the data. Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data. 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential. Lastly, goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution. Models correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution. In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years, an exponential distribution gave the best fit to prevalence data. An exponential distribution has a relatively heavy tail, thus occasional high-prevalence communities are to be expected even when infection is disappearing. A single cross-sectional survey may be able to reveal whether elimination efforts are on-track.

Original language | English (US) |
---|---|

Article number | e0003682 |

Journal | PLoS Neglected Tropical Diseases |

Volume | 9 |

Issue number | 3 |

DOIs | |

State | Published - Mar 27 2015 |

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### ASJC Scopus subject areas

- Infectious Diseases
- Public Health, Environmental and Occupational Health
- Pharmacology, Toxicology and Pharmaceutics(all)

### Cite this

*PLoS Neglected Tropical Diseases*,

*9*(3), [e0003682]. https://doi.org/10.1371/journal.pntd.0003682

**The Distribution of Ocular Chlamydia Prevalence across Tanzanian Communities Where Trachoma Is Declining.** / Rahman, Salman A.; West, Sheila K; Mkocha, Harran; Munoz, Beatriz; Porco, Travis C.; Keenan, Jeremy D.; Lietman, Thomas M.

Research output: Contribution to journal › Article

*PLoS Neglected Tropical Diseases*, vol. 9, no. 3, e0003682. https://doi.org/10.1371/journal.pntd.0003682

}

TY - JOUR

T1 - The Distribution of Ocular Chlamydia Prevalence across Tanzanian Communities Where Trachoma Is Declining

AU - Rahman, Salman A.

AU - West, Sheila K

AU - Mkocha, Harran

AU - Munoz, Beatriz

AU - Porco, Travis C.

AU - Keenan, Jeremy D.

AU - Lietman, Thomas M.

PY - 2015/3/27

Y1 - 2015/3/27

N2 - Mathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing. Trachoma control programs offer an opportunity to test this hypothesis, as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020. Local programs may benefit if a single survey could reveal whether infection was headed towards elimination. Using data from a previously-published 2009 survey, we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed. We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation. Model selection by Akaike Information Criteria (AICc) suggested the exponential distribution had the most parsimonious fit to the data. Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data. 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential. Lastly, goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution. Models correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution. In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years, an exponential distribution gave the best fit to prevalence data. An exponential distribution has a relatively heavy tail, thus occasional high-prevalence communities are to be expected even when infection is disappearing. A single cross-sectional survey may be able to reveal whether elimination efforts are on-track.

AB - Mathematical models predict an exponential distribution of infection prevalence across communities where a disease is disappearing. Trachoma control programs offer an opportunity to test this hypothesis, as the World Health Organization has targeted trachoma for elimination as a public health concern by the year 2020. Local programs may benefit if a single survey could reveal whether infection was headed towards elimination. Using data from a previously-published 2009 survey, we test the hypothesis that Chlamydia trachomatis prevalence across 75 Tanzanian communities where trachoma had been documented to be disappearing is exponentially distributed. We fit multiple continuous distributions to the Tanzanian data and found the exponential gave the best approximation. Model selection by Akaike Information Criteria (AICc) suggested the exponential distribution had the most parsimonious fit to the data. Those distributions which do not include the exponential as a special or limiting case had much lower likelihoods of fitting the observed data. 95% confidence intervals for shape parameter estimates of those distributions which do include the exponential as a special or limiting case were consistent with the exponential. Lastly, goodness-of-fit testing was unable to reject the hypothesis that the prevalence data came from an exponential distribution. Models correctly predict that infection prevalence across communities where a disease is disappearing is best described by an exponential distribution. In Tanzanian communities where local control efforts had reduced the clinical signs of trachoma by 80% over 10 years, an exponential distribution gave the best fit to prevalence data. An exponential distribution has a relatively heavy tail, thus occasional high-prevalence communities are to be expected even when infection is disappearing. A single cross-sectional survey may be able to reveal whether elimination efforts are on-track.

UR - http://www.scopus.com/inward/record.url?scp=84928822469&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84928822469&partnerID=8YFLogxK

U2 - 10.1371/journal.pntd.0003682

DO - 10.1371/journal.pntd.0003682

M3 - Article

VL - 9

JO - PLoS Neglected Tropical Diseases

JF - PLoS Neglected Tropical Diseases

SN - 1935-2727

IS - 3

M1 - e0003682

ER -