malariacontrol.net

Join malariacontrol.net

  1. Read our rules and policies
  2. Download BOINC
  3. When prompted, enter http://www.malariacontrol.net/

Returning participants

Community

Project totals and leader boards

BOINC Logo

User of the day

User profile Profile janosch13
Hi bin 36 Jahre alt (besser jung) und arbeite als Ingenieur fr Automatisierungstechnik in einem groen Kraftwerk. Habe dort etwa 200...
News

Down, and back
Dear BOINC users,

As you may have noticed, malariacontrol.net has been down since Friday the 24th. By the time you see this message, it should be up and fully operational again, but it might still need some adjustment.

Our apologies go to everyone inconvenienced: the cause of the down time was beyond our control, but we should perhaps have been better prepared to handle it. In any case, malariacontrol.net should be up and running again now, though there are a few things which may still need sorting out. Delays were mostly to give us more time to test the new server set-up and due to some non-technical reasons (lets just say life includes some joyous moments you can't miss).

The cause of the failure was simple enough: hardware failure of an old machine. We have set up the a server to take over the running of malariacontrol.net (on significantly more powerful hardware), using backups of the old machine. Unfortunately the terminal failure of the old machine means that we were forced to use backups from a few hours before the failure, and any results uploaded just before the failure have been lost. For us, this means some work will have to be resubmitted; for you, it means you may have lost some credit (hopefully we can compensate for this).

The short of it is we're back up now with more server capacity than before and keen to make up for lost time. Thanks to you for sticking around, lets get going again — and please do let us know if there are any problems due to the migration!

Diggory Hardy
Michael Tarantino
Nicolas Maire
4 Feb 2014 10:49:32 UTC · Comment


Science update part III: till June 2013
Dear malariacontrol.net user,

In our third installment on the science update, we look at how your cpu cycles helped Olivier Briët and his colleagues explore the pressing issue of how insecticide resistance might affect the cost effectiveness of an intervention, as reported in Effects of pyrethroid resistance on the cost effectiveness of a mass distribution of long-lasting insecticidal nets: a modelling study.

The effectiveness of insecticide-treated nets in preventing malaria is threatened by developing resistance against pyrethroids. Little is known about how strongly pyrethroid resistance affects the effectiveness of vector control programmes.

In this analysis, data from experimental hut studies on the effects of long-lasting, insecticidal nets (LLINs) on nine anopheline mosquito populations, with varying levels of mortality in World Health Organization susceptibility tests, were used to parameterize models. Both simple static models predicting population-level insecticidal effectiveness and protection against blood feeding, and complex dynamic epidemiological models, where LLINs decayed over time, were used. The epidemiological models, implemented in OpenMalaria, were employed to study the impact of a single mass distribution of LLINs on malaria, both in terms of episodes prevented during the effective lifetime of the batch of LLINs, and in terms of net health benefits expressed in disability-adjusted life years (DALYs) averted during that period, depending on net type (standard pyrethroid-only LLIN or pyrethroid-piperonyl butoxide combination LLIN), resistance status, coverage and pre-intervention transmission level.

The basis model features are displayed in a graphic of the useful lifetime of a single ITN distribution. As the nets age, the insecticide in the net wears out and the number of holes in the nets increases. These factors combine to limit the useful lifetime a single net distribution. Note that the slight bump in the baseline malaria level after the net distribution is no longer in effect is real: the cases averted and decreased exposure during the viable net distribution decreases immunity. With no other intervention, the episodes per person over time returns to the baseline level.

With the most resistant mosquito population, the LLIN mass distribution averted up to about 40% fewer episodes and DALYs during the effective lifetime of the batch than with fully susceptible populations. However, cost effectiveness of LLINs was more sensitive to the pre-intervention transmission level and coverage than to mosquito susceptibility status. For four out of the six Anopheles gambiae sensu lato populations where direct comparisons between standard LLINs and combination LLINs were possible, combination nets were more cost effective, despite being more expensive. With one resistant population, both net types were equally effective, and with one of the two susceptible populations, standard LLINs were more cost effective.

Despite being less effective when compared to areas with susceptible mosquito populations, standard and combination LLINs are likely to still be cost effective against malaria even in areas with strong pyrethroid resistance.

So, well done you! for contributing to this work.
30 Jul 2013 14:20:10 UTC · Comment


Science update part II: till March 2013
Dear malariacontrol.net member,

As promised, here is the second of our three part update on the science of malariacontrol.net. We look at some cost effectiveness analyses that were only possible with your donated cpu cycles.

Mass drug administration (MDA), where the entire population is treated with antimalarial drugs, and mass screening and treatment (MSAT), which involves screening the whole population of interest and only treating those who test positive, are two strategies that may have the potential to reduce P. falciparum malaria burden. Although it is more complex to organize, one would prefer to use MSAT in order to avoid over-use of drugs and contributing to the spread of drug resistance. But is MSAT likely to be a good use of resources, and if so, where? Can we put a number on it?

Decision makers need comparable information on both the effects and cost of interventions. With your help, simulations have been run to try to quantify the incremental cost per unit health gain from well-designed MSAT campaigns in different health systems and transmission settings.

For this analysis the outcome measure was the incremental cost-effectiveness ratio (ICER), expressed as dollars per malaria case averted. Cases averted by MSAT were obtained using simulation results from malariacontrol.net and costs estimated from an economic model using literature on the costs of similar interventions in sub-Saharan Africa. The calculated ICER results were compared to the ICERs of increasing case management or insecticide-treated net (ITN) coverage in each setting. Here by case management we mean doctor’s visits, hospitalization when needed and follow up care.


As you can see in the graphic, the incremental savings of each method depended very much the baseline transmission level [ recall last week’s post on EIR]. This figure suggests that MSAT was most cost-effective in settings with a moderate disease burden.


The results of your simulations showed that at low transmission MSAT was never more cost-effective than scaling up ITNs or case management and is probably not worth considering. Instead, MSAT may be more suitable at medium to high transmission levels and at moderate ITN coverage. In these settings, the cost-effectiveness of MSAT may be comparable to that of scaling up case management and ITN coverage. In all the transmission settings considered, achieving a minimal level of ITN coverage is a best buy. An interesting finding, and one that merits further investigation, is that achieving 80% ITN coverage across all settings, as per current global malaria strategies, may not be an efficient use of resources, particularly in low-transmission settings.

This study suggests that policy-makers may want to consider MSAT to reduce the malaria burden as they choose among interventions for their populations. It also shows how the malaria models can be used to simulate combinations of interventions and generate estimates of their relative cost-effectiveness. We intend to build on this type of work in the future.

If you would like more detail on this work, see the paper by Valerie Crowell and others Modelling the cost-effectiveness of mass screening and treatment for reducing Plasmodium falciparum malaria burden.

Again, thanks for all your volunteered CPU cycles – we couldn’t do it without you.
30 Jul 2013 13:27:11 UTC · Comment


Science update part I: till January 2013
It has been a busy year at malariacontrol.net. Much too busy to tell you about the good work you’ve contributed to in just one post. Therefore, this science update comes in three parts, to be published over the next few days.

This first post we will talk about some work that was published last fall, looking at how best to estimate the best way to eliminate malaria in low transmission settings.

Malaria transmission is governed by many things, but when scientists are talking about transmission, they are generally thinking of the entomological inoculation rate [EIR], that is, the average number of infected mosquito bites a person receives in a year. In some of the worst malarial areas, this number can easily be in the hundreds of bites per year.

EIR is generally measured by trapping mosquitos and seeing what percentage of them are infected with malaria and then factoring in the number of bites they give a night. For example, a catch of 20 biting Anopheles per person per night, where 16 are human-fed and 2 of those are infected with malaria sporozoites would correspond to an EIR for that day of 20 x 16/20 x (2/16) 1 = 1.68. Each individual in that area receives an average of 1.68 infective bites per night or an annual EIR 613 - an indication of very high malaria transmission. But when the transmission rate is very low (which is, in and of itself, a good thing), perhaps an EIR of 1 or 2 per year, you would need to trap many more mosquitos to get a reliable estimate of the percentage of them carrying malaria. Further, one should not assume that the overall dynamics of transmission would be the same in these low transmission areas compared to the higher, better studied ones.

Erin Stuckey at the Swiss TPH used malariacontrol.net to explore transmission dynamics in a low-transmission setting, the Rachuonyo South highlands above the shores of Lake Victoria in Kenya. One of the reasons we run models is to try to understand which factors have the most impact on outcome of interest (in this case, malaria control). She found that key issues for Rachuonyo were vector biting behaviour, their susceptibility to indoor residual spraying (IRS), and the detection method used for human surveys – all of these affect the impact of interventions in areas with low and/or unstable P. falciparum transmission.

Erin also looked at the influence detection method used for surveys on the final estimate of prevalence. To address model sensitivity to the ability of a given test to detect a P. falciparum infection, an experiment was created to mimic the detection limits of a rapid diagnostic test (RDT), polymerase chain reaction (PCR), skilled microscopy, and a low-quality diagnostic such as a poor-quality RDT or unskilled microscopy. The prevalence estimate decreases with higher detection limits, as does the stochasticity of the predictions.

This graphic from Erin's paper shows this effect of changing the detection limit (number of parasites per microliter) at which the survey is able to detect P. falciparum infection on the simulated number of P. falciparum infections in a population of 10,000 individuals for

    a) baseline model with a detection limit of 200, equivalent to RDT;
    b) detection limit of 40, equivalent to PCR;
    c) detection limit of 100, equivalent to skilled microscopy; and
    d) detection limit of 500, equivalent to a poor quality diagnostic.



The implication is that if RDTs used in surveys perform poorly, whether the result of low quality manufacturing or improper storage conditions or use, according to simulation results up to half of infected individuals would be misclassified.

Decision makers need some kind of guidance on where to best put their efforts at malaria control. We need simulations such as these especially when the field data are sparse. In this case, measuring EIR through mosquito collection may not be the optimal way to define transmission in areas with low, unstable transmission, but simulation results from models such as OpenMalaria can help fill the gap between what we can realistically measure in the field and what we need to know about a given area for malaria control.

26 Jul 2013 9:41:40 UTC · Comment

Science update August 2012
Read about recent adventures in model-fitting, and how your simulation runs were used to analyse which factors were most important to determine the effective lifetime of long-lasting insecticide treated nets. more... 29 Aug 2012 7:59:23 UTC · Comment


... more

News is available as an RSS feed   RSS

News is available as an RSS feed XML.





Copyright © 2014 africa@home