Severe acute respiratory syndrome (SARS) is a recently described illness of humans that has spread widely over the past 6 months. With the use of detailed epidemiologic data from Singapore and epidemic curves from other settings, we estimated the reproductive number for SARS in the absence of interventions and in the presence of control efforts. We estimate that a single infectious case of SARS will infect about three secondary cases in a population that has not yet instituted control measures. Public-health efforts to reduce transmission are expected to have a substantial impact on reducing the size of the epidemic.

Non-causal associations between exposures and outcomes are a threat to validity of causal inference in observational studies. Many techniques have been developed for study design and analysis to identify and eliminate such errors. Such problems are not expected to compromise experimental studies, where careful standardization of conditions (for laboratory work) and randomization (for population studies) should, if applied properly, eliminate most such non-causal associations. We argue, however, that a routine precaution taken in the design of biological laboratory experiments—the use of “negative controls”—is designed to detect both suspected and unsuspected sources of spurious causal inference. In epidemiology, analogous negative controls help to identify and resolve confounding as well as other sources of error, including recall bias or analytic flaws. We distinguish two types of negative controls (exposure controls and outcome controls), describe examples of each type from the epidemiologic literature, and identify the conditions for the use of such negative controls to detect confounding. We conclude that negative controls should be more commonly employed in observational studies, and that additional work is needed to specify the conditions under which negative controls will be sensitive detectors of other sources of error in observational studies.

A critical question in tuberculosis control is why some strains of Mycobacterium tuberculosis are preferentially associated with multiple drug resistances. We demonstrate that M. tuberculosis strains from Lineage 2 (East Asian lineage and Beijing sublineage) acquire drug resistances in vitro more rapidly than M. tuberculosis strains from Lineage 4 (Euro-American lineage) and that this higher rate can be attributed to a higher mutation rate. Moreover, the in vitro mutation rate correlates well with the bacterial mutation rate in humans as determined by whole genome sequencing of clinical isolates. Finally, using a stochastic mathematical model, we demonstrate that the observed differences in mutation rate predict a substantially higher probability that patients infected with a drug susceptible Lineage 2 strain will harbor multidrug resistant bacteria at the time of diagnosis. These data suggest that interventions to prevent the emergence of drug resistant tuberculosis should target bacterial as well as treatment-related risk factors.

BackgroundThe response to the next influenza pandemic will likely include extensive use of antiviral drugs (mainly oseltamivir), combined with other transmission-reducing measures. Animal and in vitro studies suggest that some strains of influenza may become resistant to oseltamivir while maintaining infectiousness (fitness). Use of antiviral agents on the scale anticipated for the control of pandemic influenza will create an unprecedented selective pressure for the emergence and spread of these strains. Nonetheless, antiviral resistance has received little attention when evaluating these plans.Methods and FindingsWe designed and analyzed a deterministic compartmental model of the transmission of oseltamivir-sensitive and -resistant influenza infections during a pandemic. The model predicts that even if antiviral treatment or prophylaxis leads to the emergence of a transmissible resistant strain in as few as 1 in 50,000 treated persons and 1 in 500,000 prophylaxed persons, widespread use of antivirals may strongly promote the spread of resistant strains at the population level, leading to a prevalence of tens of percent by the end of a pandemic. On the other hand, even in circumstances in which a resistant strain spreads widely, the use of antivirals may significantly delay and/or reduce the total size of the pandemic. If resistant strains carry some fitness cost, then, despite widespread emergence of resistance, antivirals could slow pandemic spread by months or more, and buy time for vaccine development; this delay would be prolonged by nondrug control measures (e.g., social distancing) that reduce transmission, or use of a stockpiled suboptimal vaccine. Surprisingly, the model suggests that such nondrug control measures would increase the proportion of the epidemic caused by resistant strains.ConclusionsThe benefits of antiviral drug use to control an influenza pandemic may be reduced, although not completely offset, by drug resistance in the virus. Therefore, the risk of resistance should be considered in pandemic planning and monitored closely during a pandemic.

Background Multidrug-resistant tuberculosis (MDR-TB) threatens to reverse recent reductions in global tuberculosis (TB) incidence. Although children under 15 years of age constitute >25% of the worldwide population, the global incidence of MDR-TB disease in children has never been quantified. Methods Our approach for estimating regional and global annual incidence of MDR-TB in children required development of two models: one to estimate the setting-specific risk of MDR-TB among child TB cases, and a second to estimate the setting-specific incidence of TB disease in children. The model for MDR-TB risk among children with TB required a systematic literature review. We multiplied the setting-specific estimates of MDR-TB risk and TB incidence to estimate regional and global incidence of MDR-TB disease in children in 2010. Findings We identified 3,403 papers, of which 97 studies met inclusion criteria for the systematic review of MDR-TB risk. Thirty-one studies reported the risk of MDR-TB among both children and treatment-naïve adults with TB and were used for evaluating the linear association between MDR-TB risk in these two patient groups. We found that the setting-specific risk of MDR-TB was nearly identical in children and treatment-naïve adults with TB, consistent with the assertion that MDR-TB in both groups reflects the local risk of transmitted MDR-TB. Applying these calculated risks, we estimated that around 1,000,000 (95% Confidence Interval: 938,000 – 1,055,000) children developed TB disease in 2010, among whom 32,000 (95% Confidence Interval: 26,000 – 39,000) had MDR-TB. Interpretation Our estimates highlight a massive detection gap for children with TB and MDR-TB disease. Future estimates can be refined as more and better TB data and new diagnostic tools become available.

Mathematical models have recently been used to predict the future burden of multidrug-resistant tuberculosis (MDRTB). These models suggest the threat of multidrug resistance to TB control will depend on the relative 'fitness' of MDR strains and imply that if the average fitness of MDR strains is considerably less than that of drug-sensitive strains, the emergence of resistance will not jeopardize the success of tuberculosis control efforts. Multidrug resistance in M. tuberculosis is conferred by the sequential acquisition of a number of different single-locus mutations that have been shown to have heterogeneous phenotypic effects. Here we model the impact of initial fitness estimates on the emergence of MDRTB assuming that the relative fitness of MDR strains is heterogeneous. We find that even when the average relative fitness of MDR strains is low and a well-functioning control program is in place, a small subpopulation of a relatively fit MDR strain may eventually outcompete both the drug-sensitive strains and the less fit MDR strains. These results imply that current epidemiological measures and short-term trends in the burden of MDRTB do not provide evidence that MDRTB strains can be contained in the absence of specific efforts to limit transmission from those with MDR disease.

Nicolas Menzies and colleagues investigate the potential impact and cost-effectiveness of implementing Xpert MTB/RIF for diagnosing tuberculosis in five southern African countries.

In most pathogens, multiple strains are maintained within host populations. Quantifying the mechanisms underlying strain coexistence would aid public health planning and improve understanding of disease dynamics. We argue that mathematical models of strain coexistence, when applied to indistinguishable strains, should meet criteria for both ecological neutrality and population genetic neutrality. We show that closed clonal transmission models which can be written in an “ancestor-tracing” form that meets the former criterion will also satisfy the latter. Neutral models can be a parsimonious starting point for studying mechanisms of strain coexistence; implications for past and future studies are discussed.