Quantifying pathogen surveillance using temporal genomic data

Early prediction of antigenic transitions for influenza A/H3N2

Influenza A/H3N2 is a rapidly evolving virus which experiences major antigenic transitions every two to eight years. Anticipating the timing and outcome of transitions is critical to developing effective seasonal influenza vaccines. Using a published phylodynamic model of influenza transmission, we identified indicators of future evolutionary success for an emerging antigenic cluster and quantified fundamental trade-offs in our ability to make such predictions. The eventual fate of a new cluster depends on its initial epidemiological growth rate––which is a function of mutational load and population susceptibility to the cluster––along with the variance in growth rate across co-circulating viruses. Logistic regression can predict whether a cluster at 5% relative frequency will eventually succeed with ~80% sensitivity, providing up to eight months advance warning. As a cluster expands, the predictions improve while the lead-time for vaccine development and other interventions decreases. However, attempts to make comparable predictions from 12 years of empirical influenza surveillance data, which are far sparser and more coarse-grained, achieve only 56% sensitivity. By expanding influenza surveillance to obtain more granular estimates of the frequencies of and population-wide susceptibility to emerging viruses, we can better anticipate major antigenic transitions. This provides added incentives for accelerating the vaccine production cycle to reduce the lead time required for strain selection.

The efficacy of annual seasonal influenza vaccines depends on selecting the strain that best matches circulating viruses. This selection takes place 9–12 months prior to the influenza season. To advise this decision, we used an influenza A/H3N2 phylodynamic simulation to explore how reliably and how far in advance can we identify strains that will dominate future influenza seasons? What data should we collect to accelerate and improve the accuracy of such forecasts? And importantly, what is the gap between the theoretical limit of prediction and prediction based on current influenza surveillance? Our results suggest that even with detailed virological information, the tight race between the antigenic turnover dynamics and the vaccine development timeline limits early detection of emerging viruses. Predictions based on current influenza surveillance do not achieve the theoretical limit and thus our results provide impetus for denser sampling and the development of rapid methods for estimating viral fitness.

Molecular evolution methods to study HIV-1 epidemics

Nucleotide sequences of HIV isolates are obtained routinely to evaluate the presence of resistance mutations to antiretroviral drugs. But, beyond their clinical use, these and other viral sequences include a wealth of information that can be used to better understand and characterize the epidemiology of HIV in relevant populations. In this review, we provide a brief overview of the main methods used to analyze HIV sequences, the data bases where reference sequences can be obtained, and some caveats about the possible applications for public health of these analyses, along with some considerations about their limitations and correct usage to derive robust and reliable conclusions.

Rapid and sensitive detection of H7N9 avian influenza virus by use of reverse transcription-loop-mediated isothermal amplification

An epidemic of human H7N9 influenza virus infection recently emerged in China whose clinical features include high mortality and which has also resulted in serious economic loss. The novel reassortant avian-origin influenza A (H7N9) virus which was the causative agent of this epidemic raised the possibility of triggering a large-scale influenza pandemic worldwide. It seemed likely that fast molecular detection assays specific for this virus would be in great demand. Here, we report a one-step reverse transcription-loop-mediated isothermal amplification (RT-LAMP) method for rapid detection of the hemagglutinin (HA) and neuraminidase (NA) genes of H7N9 virus, the minimum detection limit of which was evaluated using in vitro RNA transcription templates. In total, 135 samples from clinical specimens (from either patients or poultry) were tested using this method in comparison with the real-time PCR recommended by the World Health Organization (WHO). Our results showed that (i) RT-LAMP-based trials can be completed in approximately 12 to 23 min and (ii) the detection limit for the H7 gene is around 10 copies per reaction, similar to that of the real-time PCR, whereas the detection limit for its counterpart the N9 gene is 5 copies per reaction, a 100-fold-higher sensitivity than the WHO-recommended method. Indeed, this excellent performance of our method was also validated by the results for a series of clinical specimens. Therefore, we believe that the simple, fast, and sensitive method of RT-LAMP might be widely applied for detection of H7N9 infections and may play a role in prevention of an influenza pandemic.