Microbe interactions undermine predictions

Developing Research in Infectious and Tropical Diseases in Africa: The Paradigm of Senegal

Infectious diseases represent one of the greatest potential barriers to achievement of the third Sustainable Development Goals in African countries and around the world because they continue to pose major public health challenges. The surveillance of infectious diseases has recently assumed greater importance in most African countries, both because of the emergence of infectious diseases and because strains of pathogens that cause tuberculosis, malaria, cholera, dysentery, and pneumonia have developed resistance to common and inexpensive antimicrobial drugs. However, data on the pathogen-specific causes of infectious diseases are limited. Developing research in infectious and tropical diseases in Africa is urgently needed to better describe the distribution of pathogen-borne diseases and to know which pathogens actually cause fever. This research is critical for guiding treatment and policies in Africa. More effective diagnostics are also needed for these diseases, which often are misdiagnosed or diagnosed too late. A comprehensive review of this type of research is presented here.

Building test data from real outbreaks for evaluating detection algorithms

Benchmarking surveillance systems requires realistic simulations of disease outbreaks. However, obtaining these data in sufficient quantity, with a realistic shape and covering a sufficient range of agents, size and duration, is known to be very difficult. The dataset of outbreak signals generated should reflect the likely distribution of authentic situations faced by the surveillance system, including very unlikely outbreak signals. We propose and evaluate a new approach based on the use of historical outbreak data to simulate tailored outbreak signals. The method relies on a homothetic transformation of the historical distribution followed by resampling processes (Binomial, Inverse Transform Sampling Method-ITSM, Metropolis-Hasting Random Walk, Metropolis-Hasting Independent, Gibbs Sampler, Hybrid Gibbs Sampler). We carried out an analysis to identify the most important input parameters for simulation quality and to evaluate performance for each of the resampling algorithms. Our analysis confirms the influence of the type of algorithm used and simulation parameters (i.e. days, number of cases, outbreak shape, overall scale factor) on the results. We show that, regardless of the outbreaks, algorithms and metrics chosen for the evaluation, simulation quality decreased with the increase in the number of days simulated and increased with the number of cases simulated. Simulating outbreaks with fewer cases than days of duration (i.e. overall scale factor less than 1) resulted in an important loss of information during the simulation. We found that Gibbs sampling with a shrinkage procedure provides a good balance between accuracy and data dependency. If dependency is of little importance, binomial and ITSM methods are accurate. Given the constraint of keeping the simulation within a range of plausible epidemiological curves faced by the surveillance system, our study confirms that our approach can be used to generate a large spectrum of outbreak signals.

Motorcycles, Cell Phones, and Electricity Can Dramatically Change the Epidemiology of Infectious Disease in Africa

AbstractSome observations and recent publications demonstrated, particularly in Africa, the potential influence that low-cost motorcycles, cell phones, and even widespread electrification could have on the evolution of infectious diseases, particularly zoonoses. Our reflections support the conclusion that we should focus on the real-time surveillance systems including alerting systems leading to a rapid and flexible response rather than the strongly limited modeling of infectious diseases because of the continuous evolution of microorganisms, as well as changes in the environment and human habits that are unpredictable.

EPIMIC: A Simple Homemade Computer Program for Real-Time EPIdemiological Surveillance and Alert Based on MICrobiological Data

BACKGROUND AND AIMS

Infectious diseases (IDs) are major causes of morbidity and mortality and their surveillance is critical. In 2002, we implemented a simple and versatile homemade tool, named EPIMIC, for the real-time systematic automated surveillance of IDs at Marseille university hospitals, based on the data from our clinical microbiology laboratory, including clinical samples, tests and diagnoses.

METHODS

This tool was specifically designed to detect abnormal events as IDs are rarely predicted and modeled. EPIMIC operates using Microsoft Excel software and requires no particular computer skills or resources. An abnormal event corresponds to an increase above, or a decrease below threshold values calculated based on the mean of historical data plus or minus 2 standard deviations, respectively.

RESULTS

Between November 2002 and October 2013 (11 years), 293 items were surveyed weekly, including 38 clinical samples, 86 pathogens, 79 diagnosis tests, and 39 antibacterial resistance patterns. The mean duration of surveillance was 7.6 years (range, 1 month-10.9 years). A total of 108,427 Microsoft Excel file cells were filled with counts of clinical samples, and 110,017 cells were filled with counts of diagnoses. A total of 1,390,689 samples were analyzed. Among them, 172,180 were found to be positive for a pathogen. EPIMIC generated a mean number of 0.5 alert/week on abnormal events.

CONCLUSIONS

EPIMIC proved to be efficient for real-time automated laboratory-based surveillance and alerting at our university hospital clinical microbiology laboratory-scale. It is freely downloadable from the following URL: http://www.mediterranee-infection.com/article.php?larub=157&titre=bulletin-epidemiologique (last accessed: 20/11/2015).

Modelling in infectious diseases: between haphazard and hazard

Modelling of infectious diseases is difficult, if not impossible. No epidemic has ever been truly predicted, rather than being merely noticed when it was already ongoing. Modelling the future course of an epidemic is similarly tenuous, as exemplified by ominous predictions during the last influenza pandemic leading to exaggerated national responses. The continuous evolution of microorganisms, the introduction of new pathogens into the human population and the interactions of a specific pathogen with the environment, vectors, intermediate hosts, reservoir animals and other microorganisms are far too complex to be predictable. Our environment is changing at an unprecedented rate, and human-related factors, which are essential components of any epidemic prediction model, are difficult to foresee in our increasingly dynamic societies. Any epidemiological model is, by definition, an abstraction of the real world, and fundamental assumptions and simplifications are therefore required. Indicator-based surveillance methods and, more recently, Internet biosurveillance systems can detect and monitor outbreaks of infections more rapidly and accurately than ever before. As the interactions between microorganisms, humans and the environment are too numerous and unexpected to be accurately represented in a mathematical model, we argue that prediction and model-based management of epidemics in their early phase are quite unlikely to become the norm.

Influenza A/H1N1 2009 pandemic and respiratory virus infections, Beijing, 2009-2010

To determine the role of the pandemic influenza A/H1N1 2009 (A/H1N1 2009pdm) in acute respiratory tract infections (ARTIs) and its impact on the epidemic of seasonal influenza viruses and other common respiratory viruses, nasal and throat swabs taken from 7,776 patients with suspected viral ARTIs from 2006 through 2010 in Beijing, China were screened by real-time PCR for influenza virus typing and subtyping and by multiplex or single PCR tests for other common respiratory viruses. We observed a distinctive dual peak pattern of influenza epidemic during the A/H1N1 2009pdm in Beijing, China, which was formed by the A/H1N1 2009pdm, and a subsequent influenza B epidemic in year 2009/2010. Our analysis also shows a small peak formed by a seasonal H3N2 epidemic prior to the A/H1N1 2009pdm peak. Parallel detection of multiple respiratory viruses shows that the epidemic of common respiratory viruses, except human rhinovirus, was delayed during the pandemic of the A/H1N1 2009pdm. The H1N1 2009pdm mainly caused upper respiratory tract infections in the sampled patients; patients infected with H1N1 2009pdm had a higher percentage of cough than those infected with seasonal influenza or other respiratory viruses. Our findings indicate that A/H1N1 2009pdm and other respiratory viruses except human rhinovirus could interfere with each other during their transmission between human beings. Understanding the mechanisms and effects of such interference is needed for effective control of future influenza epidemics.