Match!
Sarah F. McGough
Harvard University
Disease surveillancePopulationAntibioticsBiologyAntibiotic resistance
7Publications
2H-index
90Citations
What is this?
Publications 9
Newest
#1Sarah F. McGough (Harvard University)H-Index: 2
#2Michael A. Johansson (CDC: Centers for Disease Control and Prevention)H-Index: 25
Last. Nicolas A. Menzies (Harvard University)H-Index: 11
view all 4 authors...
Achieving accurate, real-time estimates of disease activity is challenged by delays in case reporting. "Nowcast" approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission-that future cases are intrinsical...
2 CitationsSource
#1Emily L. Aiken (Harvard University)H-Index: 1
#2Sarah F. McGough (Harvard University)H-Index: 2
Last. Mauricio Santillana (Harvard University)H-Index: 16
view all 7 authors...
Understanding the behavior of emerging disease outbreaks in, or ahead of, real-time could help healthcare officials better design interventions to mitigate impacts on affected populations. Most healthcare-based disease surveillance systems, however, have significant inherent reporting delays due to data collection, aggregation, and distribution processes. Recent work has shown that machine learning methods leveraging a combination of traditionally collected epidemiological information and novel ...
1 CitationsSource
#1Sarah F. McGough (Harvard University)H-Index: 2
#2Derek R. MacFadden (U of T: University of Toronto)H-Index: 11
Last. Mauricio Santillana (Harvard University)H-Index: 16
view all 5 authors...
Source
#1Sarah F. McGough (Harvard University)H-Index: 2
#2Cesar Leonardo Clemente (Tec: Monterrey Institute of Technology and Higher Education)H-Index: 1
Last. Mauricio Santillana (Harvard University)H-Index: 16
view all 4 authors...
Transmission of dengue fever depends on a complex interplay of human, climate, and mosquito dynamics, which often change in time and space. It is well known that disease dynamics are highly influenced by a population9s susceptibility to infection and microclimates, small-area climatic conditions which create environments favorable for the breeding and survival of the mosquito vector. Here, we present a novel machine learning dengue forecasting approach, which, dynamically in time and adaptively ...
Source
#1Sarah F. McGough (Harvard University)H-Index: 2
#2Michael A. Johansson (CDC: Centers for Disease Control and Prevention)H-Index: 25
Last. Nicolas A. Menzies (Harvard University)H-Index: 11
view all 4 authors...
Abstract Delays in case reporting are common to disease surveillance systems, making it difficult to track diseases in real-time. “Nowcast” approaches attempt to estimate the complete case counts for a given reporting date, using a time series of case reports that is known to be incomplete due to reporting delays. Modeling the reporting delay distribution is a common feature of nowcast approaches. However, many nowcast approaches ignore a crucial feature of infectious disease transmission—that f...
Source
#1Sarah F. McGough (Harvard University)H-Index: 2
#2Derek R MacFadden (Harvard University)H-Index: 4
Last. Mauricio Santillana (Boston Children's Hospital)H-Index: 16
view all 5 authors...
Background. Widely recognized as a major public health threat globally, the rapid increase of antibiotic resistance in bacteria could soon render our most effective method to combat infections obsolete. Factors influencing the burden of resistance in human populations remain poorly described, though temperature is known to play an important role in mechanisms at the bacterial level. Methods. We performed an ecologic analysis of country level antibiotic resistance prevalence in 3 common bacterial...
2 CitationsSource
#1Derek R. MacFadden (Harvard University)H-Index: 11
#2Sarah F. McGough (Harvard University)H-Index: 2
Last. John S. Brownstein (Harvard University)H-Index: 57
view all 5 authors...
Bacteria that cause infections in humans can develop or acquire resistance to antibiotics commonly used against them1,2. Antimicrobial resistance (in bacteria and other microbes) causes significant morbidity worldwide, and some estimates indicate the attributable mortality could reach up to 10 million by 20502–4. Antibiotic resistance in bacteria is believed to develop largely under the selective pressure of antibiotic use; however, other factors may contribute to population level increases in a...
34 CitationsSource
#1Anthony E. Kiszewski (Bentley University)H-Index: 17
#2Marcia C. Castro (Harvard University)H-Index: 30
Last. Sarah F. McGough (Harvard University)H-Index: 2
view all 3 authors...
Source
#1Sarah F. McGough (Harvard University)H-Index: 2
#2John S. Brownstein (Harvard University)H-Index: 57
Last. Mauricio Santillana (Harvard University)H-Index: 16
view all 4 authors...
Background Over 400,000 people across the Americas are thought to have been infected with Zika virus as a consequence of the 2015–2016 Latin American outbreak. Official government-led case count data in Latin America are typically delayed by several weeks, making it difficult to track the disease in a timely manner. Thus, timely disease tracking systems are needed to design and assess interventions to mitigate disease transmission. Methodology/Principal Findings We combined information from Zika...
52 CitationsSource
1